A very simple framework for state-of-the-art Natural Language Processing (NLP)


alt text

PyPI version GitHub Issues Contributions welcome License: MIT

A very simple framework for state-of-the-art NLP. Developed by Humboldt University of Berlin and friends.

  • IMPORTANT: (30.08.2020) We moved our models to a new server. Please update your Flair to the newest version!

Flair is:

  • A powerful NLP library. Flair allows you to apply our state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS), sense disambiguation and classification, with support for a rapidly growing number of languages.

  • A biomedical NER library. Flair has special support for biomedical data with state-of-the-art models for biomedical NER and support for over 32 biomedical datasets.

  • A text embedding library. Flair has simple interfaces that allow you to use and combine different word and document embeddings, including our proposed Flair embeddings, BERT embeddings and ELMo embeddings.

  • A PyTorch NLP framework. Our framework builds directly on PyTorch, making it easy to train your own models and experiment with new approaches using Flair embeddings and classes.

Now at version 0.7!

Comparison with State-of-the-Art

Flair outperforms the previous best methods on a range of NLP tasks:

Task Language Dataset Flair Previous best
Named Entity Recognition English Conll-03 93.18 (F1) 92.22 (Peters et al., 2018)
Named Entity Recognition English Ontonotes 89.3 (F1) 86.28 (Chiu et al., 2016)
Emerging Entity Detection English WNUT-17 49.49 (F1) 45.55 (Aguilar et al., 2018)
Part-of-Speech tagging English WSJ 97.85 97.64 (Choi, 2016)
Chunking English Conll-2000 96.72 (F1) 96.36 (Peters et al., 2017)
Named Entity Recognition German Conll-03 88.27 (F1) 78.76 (Lample et al., 2016)
Named Entity Recognition German Germeval 84.65 (F1) 79.08 (Hänig et al, 2014)
Named Entity Recognition Dutch Conll-02 92.38 (F1) 81.74 (Lample et al., 2016)
Named Entity Recognition Polish PolEval-2018 86.6 (F1)
(Borchmann et al., 2018)
85.1 (PolDeepNer)

Here's how to reproduce these numbers using Flair. You can also find detailed evaluations and discussions in our papers:

Quick Start

Requirements and Installation

The project is based on PyTorch 1.1+ and Python 3.6+, because method signatures and type hints are beautiful. If you do not have Python 3.6, install it first. Here is how for Ubuntu 16.04. Then, in your favorite virtual environment, simply do:

pip install flair

Example Usage

Let's run named entity recognition (NER) over an example sentence. All you need to do is make a Sentence, load a pre-trained model and use it to predict tags for the sentence:

from flair.data import Sentence
from flair.models import SequenceTagger

# make a sentence
sentence = Sentence('I love Berlin .')

# load the NER tagger
tagger = SequenceTagger.load('ner')

# run NER over sentence

Done! The Sentence now has entity annotations. Print the sentence to see what the tagger found.

print('The following NER tags are found:')

# iterate over entities and print
for entity in sentence.get_spans('ner'):

This should print:

Sentence: "I love Berlin ." - 4 Tokens

The following NER tags are found:

Span [3]: "Berlin"   [− Labels: LOC (0.9992)]


We provide a set of quick tutorials to get you started with the library:

The tutorials explain how the base NLP classes work, how you can load pre-trained models to tag your text, how you can embed your text with different word or document embeddings, and how you can train your own language models, sequence labeling models, and text classification models. Let us know if anything is unclear.

There is also a dedicated landing page for our biomedical NER and datasets with installation instructions and tutorials.

There are also good third-party articles and posts that illustrate how to use Flair:

Citing Flair

Please cite the following paper when using Flair:

  title={Contextual String Embeddings for Sequence Labeling},
  author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
  booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
  pages     = {1638--1649},
  year      = {2018}

If you use the pooled version of the Flair embeddings (PooledFlairEmbeddings), please cite:

  title={Pooled Contextualized Embeddings for Named Entity Recognition},
  author={Akbik, Alan and Bergmann, Tanja and Vollgraf, Roland},
  booktitle = {{NAACL} 2019, 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics},
  pages     = {724–728},
  year      = {2019}


Please email your questions or comments to Alan Akbik.


Thanks for your interest in contributing! There are many ways to get involved; start with our contributor guidelines and then check these open issues for specific tasks.

For contributors looking to get deeper into the API we suggest cloning the repository and checking out the unit tests for examples of how to call methods. Nearly all classes and methods are documented, so finding your way around the code should hopefully be easy.

Running unit tests locally

You need Pipenv for this:

pipenv install --dev && pipenv shell
pytest tests/

To run integration tests execute:

pytest --runintegration tests/

The integration tests will train small models. Afterwards, the trained model will be loaded for prediction.

To also run slow tests, such as loading and using the embeddings provided by flair, you should execute:

pytest --runslow tests/


The MIT License (MIT)

Flair is licensed under the following MIT license: The MIT License (MIT) Copyright © 2018 Zalando SE, https://tech.zalando.com

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.


  • Flair 0.5 features

    Flair 0.5 features

    Here, I'd like to collect some ideas for features that we would like to see in the next version of Flair.


    • [x] Refactor data loading methods. We currently load the entire training data set into memory, but this is a problem for large datasets (#458 #457) and may also cause bottlenecks in GPU usage. Idea is to use the DataLoader abstraction (as currently used in the LanguageModelTrainer) for asynchronous loading from disk. This should make training over large datasets possible and may also significantly improve training speed.
    • [x] Refactor flair.nn.Model and ModelTrainer. The ModelTrainer currently supports training SequenceLabeler and TextClassification classes, but community members have suggested other tasks, such as regression (#440) or seq2seq (#560). The flair.nn.Model interface needs to be simplified (fewer methods) and generalized in such a way that implementing this interface will immediately enable training using the ModelTrainer class (see also #474).
    • [ ] Multi-Task Learning: This one has been on our list for a while, but we'd like to add simple methods for training multiple tasks at the same time. To do this, we may need to refactor the embeddings classes to make it easier to expose internal states (see #524).
    • [x] Tokenization. Right now, we use segtok for tokenization, but maybe we can include other tokenizers (#394), perhaps even our own trained over the UD corpora.
    • [ ] Multi-GPU support: With the changes to the new CUDA semantics introduced in 0.4.1 we can now look into multi-GPU support.

    Any other ideas? Please let us know!

    A side note: In March, a few of us will be out of office for vacations, so development will likely slow down a bit. But come April, we'll start working full steam on 0.5 :)

    opened by alanakbik 55
  • Comparison between BERT, ELMo, and Flair embeddings

    Comparison between BERT, ELMo, and Flair embeddings

    We want to collect experiments here that compare BERT, ELMo, and Flair embeddings. So if you have any findings on which embedding type work best on what kind of task, we would be more than happy if you share your results. We are also going to run some experiments and share our results here.

    help wanted question wontfix 
    opened by tabergma 42
  • pytorch-pretrained-bert to pytorch-transformers upgrade

    pytorch-pretrained-bert to pytorch-transformers upgrade


    the upcoming 1.0 version of pytorch-pretrained-bert will introduce several API changes, new models and even a name change to pytorch-transformers.

    After the final 1.0 release, flair could support 7 different Transformer-based architectures:

    • [x] BERT -> BertEmbeddings
    • [x] OpenAI GPT -> OpenAIGPTEmbeddings
    • [x] OpenAI GPT-2 -> OpenAIGPT2Embeddings 🛡️
    • [x] Transformer-XL -> TransformerXLEmbeddings
    • [x] XLNet -> XLNetEmbeddings 🛡️
    • [x] XLM -> XLMEmbeddings 🛡️
    • [x] RoBERTa -> RoBERTaEmbeddings 🛡️ (currently not covered by pytorch-transformers)

    🛡️ indicates a new embedding class for flair.

    It also introduces an universal API for all models, so quite a few changes in flair are necessary so support both old and new embedding classes.

    This issue tracks the implementation status for all 6 embedding classes 😊

    opened by stefan-it 39
  • Spanish LM

    Spanish LM

    Hello, I just trained a Spanish LM. I wonder if it is a good enough one. What are the ways for you to test if it is a good enough LM? For example, what do you get for loss in the English model? What does ppl stand for?

    This is what I got for the very last split.

    Split 10 - (08:27:57) (08:29:14) | split 10 / 9 | 100/ 555 batches | ms/batch 11655.58 | loss 1.37 | ppl 3.95 | split 10 / 9 | 200/ 555 batches | ms/batch 11570.46 | loss 1.36 | ppl 3.89 | split 10 / 9 | 300/ 555 batches | ms/batch 11550.08 | loss 1.35 | ppl 3.88 | split 10 / 9 | 400/ 555 batches | ms/batch 11563.46 | loss 1.35 | ppl 3.86 | split 10 / 9 | 500/ 555 batches | ms/batch 11523.42 | loss 1.35 | ppl 3.86 training done! (10:16:09) best loss so far 1.26

    | end of split 1 / 9 | epoch 0 | time: 7542.77s | valid loss 1.26 | valid ppl 3.52 | learning rate 20.00

    language model new language 
    opened by iamyihwa 37
  • Multilingual NER

    Multilingual NER

    When I use multilingual embedding for NER till now I have used the input text only in English and the model gave me good inference results in Spanish, however now I have also small number of tagged samples in Spanish (less than 1000 sentences) which number is too low to build a Spanish NER model. Thus, my question is if I can combine both english and spanish samples for training? Is this possible and any thoughts about the accuracy of this kind of mixed language training data.

    Remark: The entities which I use are not the standard ones, I have custom entities to train the NER model.

    Thanks in advance, Igor

    question wontfix 
    opened by igormis 36
  • Error when creating embeddings - HEAD request to S3 bucket returns 404

    Error when creating embeddings - HEAD request to S3 bucket returns 404

    Hello, I have a problem loading Word/FlairEmbeddings for English and German languages located at the urls: "https://s3.eu-central-1.amazonaws.com/alan-nlp/resources/embeddings/xxxxxxxxx.pt".

    When following Tutorial 3, trying to create these embeddings gives the following error:

    OSError: HEAD request failed for url https://s3.eu-central-1.amazonaws.com/alan-nlp/resources/embeddings/glove.gensim.vectors.npy with status code 404

    Making a simple HEAD request to that url outside of flair returns 404, so it looks like the embeddings are not located there anymore ?

    To Reproduce

    from flair.embeddings import WordEmbeddings glove_embedding = WordEmbeddings('glove')

    I would appreciate your help, Thank you !

    opened by cibeah 30
  • Arabic LM

    Arabic LM

    Hello, I tried to generate a Language model for Arabic using Flair, but it seems not working as expected. I used the Leipzig Corpora Collection as my training corpus. It contains 1M sentences in Arabic. Here is the code used for the training: ############### language_model = LanguageModel(dictionary, is_forward_lm, hidden_size=512, nlayers=1)

    train your language model

    trainer = LanguageModelTrainer(language_model, corpus)

    trainer.train('resources/taggers/language_model', sequence_length=250, mini_batch_size=100, max_epochs=10) ############## Once finished, when I try to generate text via the script provided with Flair, I got this display: text generation arabic

    Should I preprocess my dataset before the training or It's just an underfitting issue? Please advice on this?

    wontfix new language 
    opened by Yugioh1984 29
  • GH-1021: Compute Flair embeddings over original string and without spaces after token

    GH-1021: Compute Flair embeddings over original string and without spaces after token

    Now, space is inserted after every token despite whitespace_after value. So sentences being embedded do not look the same as normal text on which Flair language models (embeddings) were trained. Also, hidden state is taken as embedding after consuming a space.

    This PR computes embeddings over text with original white spaces. I am not sure if this approach is better in downstream tasks, but it is consistent with trained Flair language model.

    Initial experiments shows that calculating token embedding without space gives better results. Using whitespace_after information is a little worse. First epoch of training with fixed seeds:

    2020-01-18 21:54:41,014 epoch 1 - iter 43/431 - loss 12.58697907 - samples/sec: 104.66
    2020-01-18 21:54:54,871 epoch 1 - iter 86/431 - loss 8.39764844 - samples/sec: 99.44
    2020-01-18 21:55:08,917 epoch 1 - iter 129/431 - loss 6.48929740 - samples/sec: 98.12
    2020-01-18 21:55:23,167 epoch 1 - iter 172/431 - loss 5.37756264 - samples/sec: 96.67
    2020-01-18 21:55:36,985 epoch 1 - iter 215/431 - loss 4.63144744 - samples/sec: 99.72
    2020-01-18 21:55:50,898 epoch 1 - iter 258/431 - loss 4.14733414 - samples/sec: 99.01
    2020-01-18 21:56:04,915 epoch 1 - iter 301/431 - loss 3.76650934 - samples/sec: 98.27
    2020-01-18 21:56:18,543 epoch 1 - iter 344/431 - loss 3.50686799 - samples/sec: 101.09
    2020-01-18 21:56:32,710 epoch 1 - iter 387/431 - loss 3.27343043 - samples/sec: 97.24
    2020-01-18 21:56:46,579 epoch 1 - iter 430/431 - loss 3.07420425 - samples/sec: 99.32
    2020-01-18 21:56:46,824 ----------------------------------------------------------------------------------------------------
    2020-01-18 21:56:46,824 EPOCH 1 done: loss 3.0724 - lr 0.1000
    2020-01-18 21:57:00,554 DEV : loss 0.501313328742981 - score 0.9818

    vs. with space after every token:

    2020-01-18 21:50:48,533 epoch 1 - iter 43/431 - loss 12.42667790 - samples/sec: 103.46
    2020-01-18 21:51:02,377 epoch 1 - iter 86/431 - loss 8.29389394 - samples/sec: 99.53
    2020-01-18 21:51:16,640 epoch 1 - iter 129/431 - loss 6.39272680 - samples/sec: 96.59
    2020-01-18 21:51:31,172 epoch 1 - iter 172/431 - loss 5.28999498 - samples/sec: 94.78
    2020-01-18 21:51:45,272 epoch 1 - iter 215/431 - loss 4.54830917 - samples/sec: 97.70
    2020-01-18 21:51:59,159 epoch 1 - iter 258/431 - loss 4.06829357 - samples/sec: 99.19
    2020-01-18 21:52:13,226 epoch 1 - iter 301/431 - loss 3.69254654 - samples/sec: 97.92
    2020-01-18 21:52:26,832 epoch 1 - iter 344/431 - loss 3.43669727 - samples/sec: 101.30
    2020-01-18 21:52:41,262 epoch 1 - iter 387/431 - loss 3.21065094 - samples/sec: 95.45
    2020-01-18 21:52:55,442 epoch 1 - iter 430/431 - loss 3.01424922 - samples/sec: 97.19
    2020-01-18 21:52:55,693 ----------------------------------------------------------------------------------------------------
    2020-01-18 21:52:55,693 EPOCH 1 done: loss 3.0125 - lr 0.1000
    2020-01-18 21:53:09,711 DEV : loss 0.46862271428108215 - score 0.9831

    vs. original code (embedding includes space after):

    2020-01-18 19:11:18,763 epoch 1 - iter 43/431 - loss 13.77513606 - samples/sec: 100.09
    2020-01-18 19:11:32,758 epoch 1 - iter 86/431 - loss 9.86612977 - samples/sec: 98.49
    2020-01-18 19:11:46,444 epoch 1 - iter 129/431 - loss 7.84115263 - samples/sec: 100.66
    2020-01-18 19:12:00,549 epoch 1 - iter 172/431 - loss 6.57365022 - samples/sec: 97.65
    2020-01-18 19:12:14,326 epoch 1 - iter 215/431 - loss 5.71325676 - samples/sec: 99.98
    2020-01-18 19:12:28,293 epoch 1 - iter 258/431 - loss 5.13455269 - samples/sec: 98.62
    2020-01-18 19:12:42,390 epoch 1 - iter 301/431 - loss 4.66878698 - samples/sec: 97.71
    2020-01-18 19:12:55,841 epoch 1 - iter 344/431 - loss 4.35207718 - samples/sec: 102.41
    2020-01-18 19:13:09,813 epoch 1 - iter 387/431 - loss 4.08306071 - samples/sec: 98.59
    2020-01-18 19:13:23,596 epoch 1 - iter 430/431 - loss 3.83598182 - samples/sec: 99.94
    2020-01-18 19:13:23,847 ----------------------------------------------------------------------------------------------------
    2020-01-18 19:13:23,847 EPOCH 1 done: loss 3.8318 - lr 0.1000
    2020-01-18 19:13:37,732 DEV : loss 0.9926474690437317 - score 0.9577

    You can test it on example train.py with POS tagging on UD after applying https://github.com/flairNLP/flair/pull/1361.

    opened by djstrong 27
  • Unable to load embeddings

    Unable to load embeddings

    from flair.embeddings import FlairEmbeddings, BertEmbeddings

    init Flair embeddings

    flair_forward_embedding = FlairEmbeddings('multi-forward') flair_backward_embedding = FlairEmbeddings('multi-backward')

    init multilingual BERT

    bert_embedding = BertEmbeddings('bert-base-multilingual-cased')

    AttributeError Traceback (most recent call last) in 2 3 # init Flair embeddings ----> 4 flair_forward_embedding = FlairEmbeddings('multi-forward') 5 flair_backward_embedding = FlairEmbeddings('multi-backward') 6

    C:\Anaconda3\lib\site-packages\flair\embeddings.py in init(self, model, detach, use_cache, cache_directory) 562 self.static_embeddings = detach 563 --> 564 from flair.models import LanguageModel 565 self.lm = LanguageModel.load_language_model(model) 566 self.detach = detach

    C:\Anaconda3\lib\site-packages\flair_init_.py in 1 from . import data ----> 2 from . import models 3 from . import visual 4 from . import trainers 5

    C:\Anaconda3\lib\site-packages\flair\models_init_.py in ----> 1 from .sequence_tagger_model import SequenceTagger 2 from .language_model import LanguageModel 3 from .text_classification_model import TextClassifier

    C:\Anaconda3\lib\site-packages\flair\models\sequence_tagger_model.py in 64 65 ---> 66 class SequenceTagger(flair.nn.Model): 67 68 def init(self,

    AttributeError: module 'flair' has no attribute 'nn'

    question wontfix 
    opened by sp3234 26
  • transformer models for language model training and tag prediction instead of LSTM's

    transformer models for language model training and tag prediction instead of LSTM's

    I recently read the generative pretraining paper of openAI. According to the benchmarks, fine-tuning the openAI model on a custom dataset takes a very less amount of time compared to a LSTM based approach. Also the model has shown to improve SOTA in a lot of tasks. So I was wondering if it is possible to replace the pipeline by a transformer based model implemented by OpenAI.

    help wanted wontfix feature 
    opened by mittalsuraj18 26
  • Support for more languages?

    Support for more languages?

    Hi! Flair looks amazing. Clean code, easy to use. Thanks for making it open source!

    I was wondering if you plan to add support for more languages? Maybe all the languages whereZalando operates? :) I'm working for a company that need NLP-code that works across pretty much the same set of countries.

    Looking at different available libraries, pre-trained models for more than just English (and German in this case!), is lacking in all the other libraries.

    enhancement wontfix language model 
    opened by EmilStenstrom 26
  • Few-shot training for topic modelling with a hope of improvement from zero-shot prediction

    Few-shot training for topic modelling with a hope of improvement from zero-shot prediction

    Newbie experimenting with ZS and FS learning but get lost. So wish to get some sense out of the following question. I guess my task is a topic modelling case. I have a few class names which are domain specific (ie legal) topic names and by passing a sentence/paragraph to the TARS model and run a ZS prediction, hope to classify that into one of the class (thus a topic) names. So with base TARS model given its non-specific nature the accuracy wouldn't be very high for a domain specific sentence. That's expected but also if the sentence is completely non-related to the domain, it should map to any class name. That seem to work with the base TARS model.

    To further enhance the accuracy of topic modelling I hope to extend the base model with domain-specific samples (say around 10 per class) and implemented few shot training with a new task. Following tutorial10 and the Bahasa sample on towardsdatascience I came up with a new TARS model. Here are my observations and questions:

    1. Should I create a new label_type of which all my samples belong and just train with that? From training output it doesn't seem to have a good result as it always complain about BAD EPOCHs and no improvement. Loading and testing with the resulting final-model.pt seem to lead all sentences to one of the class labels. With the ZS model it seems to make more intuitive sense as it can predict more than 1 label with different probabilities, and more importantly, sentence not related to the classes will not result in a prediction. In the case of newly trained FS model even garbage will be modelled to a class name.

    2. If the intention is to extend on the accuracy of the base TARS model is the above the correct approach? Or should a dataset be loaded and add my training samples to the corpus and re-train? If so which dataset is relevant for topic modelling and which label_type should be used?

    3. After the above training all the example coding tell us to use the predict() function. I wonder can predict_zero_shot() be used instead with the newly trained model? What is the difference is using one or the other function in this scenario?

    4. Intuitively to me Few Shot Learning seems to mean given an existing model (tars-base), we can just load it, create a corpus with a few of our domain-specific samples, re-train it and the resulting model will enhance accuracy for prediction when the sentence is related to that specific domain. Is that the correct understanding about FSL? Is Tutorial10 intended to demonstrate that kind of effect as described?

    Much appreciated for any clarifications and assistance given.

    opened by predoctech 0
  • SequenceTagger.load error -> 'embeddings.model.embeddings.position_ids'

    SequenceTagger.load error -> 'embeddings.model.embeddings.position_ids'


    I have trained the NER model using SequenceTagger with longformer as an embedding 'allenai/longformer-base-4096' The train was finished successfully on sageMaker (amazon Linux 2)

    When I'm trying to load the model on a different machine I get the missing keys error 'embeddings.model.embeddings.position_ids'

    Would you happen to know how I can prevent this?

    This is the training script `

    embeddings = TransformerWordEmbeddings(model='allenai/longformer-base-4096',
    # 5. initialize bare-bones sequence tagger (no CRF, no RNN, no projection)
    tagger = SequenceTagger(hidden_size=256,
    # 6. initialize trainer
    trainer = ModelTrainer(tagger, corpus)
    model_path = f'round_{round_num}_full_address'
    # 7. run fine-tuning
                      mini_batch_chunk_size=1,  # remove this parameter to speed up computation if you have a big GPU


    Thank you

    opened by shahafp 0
  • Release optimizer memory and fix legacy tokenization

    Release optimizer memory and fix legacy tokenization

    This PR does 2 little things:

    • it makes it easier to clean up the gpu memory, by cleaning gradients and deleting the reference of the optimzier. Hence torch.cuda.empty_cache() will be able to clean up more parameters.
    • some (slow) tokenizers like RobertaTokenizer seem to add a [SEP] token at the end, breaking the legacy subword token mapping, which is fixed by allowing that specific token
    opened by helpmefindaname 0
  • does flairNLP support mypy

    does flairNLP support mypy

    I am using pycharm IDE and as such don't have tons of experience with python at present.

    For this line:

    from flair.data import Sentence

    I get the following warning: Mypy: Skipping analyzing "flair.data": module is installed, but missing library stubs or py.typed marker

    Is there a way to fix this error? Or flairNLP does not support mypy yet?

    opened by sillyquestion 1
  • Load own corpus and train task specific embedding

    Load own corpus and train task specific embedding

    Hi all,

    I have a corpus dataset, which contains text in the first column and a certainty label in the second column. I would like to customize a model that predicts the certainty labels. The data is already split in train, dev and test and all are located in the corpus folder as required. I have the files as csv and txt files. For both the separator is tab. As it worked for version 0.10 i tried the following:

    from flair.datasets import CSVClassificationCorpus

    this is the folder in which train, test and dev files reside

    data_folder = '/path/to/corpus'

    column_name_map = {0: "Text", 1:"certainty_Label"}
    label_type = "certainty_label"

    corpus: Corpus = CSVClassificationCorpus(data_folder, column_name_map, skip_header=True, delimiter="\t", label_type=label_type)

    This however results in the following error: [...] -> 1209 raise RuntimeError("No data provided when initializing corpus object.") 1211 # sample test data from train if none is provided 1212 if test is None and sample_missing_splits and train and not sample_missing_splits == "only_dev":

    RuntimeError: No data provided when initializing corpus object.

    I would like to train a sentence classification model, that predicts the authors certainty for a written sentence. Therefore I want to train a model with specific embeddings for this use case. Any recommendation about fixing my error and alternative approaches to load my corpus, embed it and train the model are appreciated. Many thanks :)

    P.S. a small example of the trainingdataset is attached example_trainingset.csv

    opened by ChiaraBecht 0
  • v0.11(Apr 10, 2022)

    Release 0.11 is taking us ever closer to that 1.0 release! This release makes large internal refactorings and code quality / efficiency improvements to prepare Flair 1.0. We also add new features such as text clustering, a regular expression tagger, more dataset manipulation options, and some preview features like a prototype decoder.

    New Features

    Regular Expression Tagger (#2533)

    You can now do sequence labeling in Flair with regular expressions! Simply define a RegexpTagger and add some regular expressions, like in the example below:

    # sentence with a number and two quotes
    sentence = Sentence('Figure 11 is both "too colorful" and "not informative enough".')
    # instantiate regex tagger with a quote matching pattern
    tagger = RegexpTagger(mapping=(r'(["\'])(?:(?=(\\?))\2.)*?\1', 'QUOTE'))
    # also add a number mapping
    tagger.register_labels(mapping=(r'\b\d+\b', 'NUMBER'))
    # tag sentence
    # check out matches
    for entity in sentence.get_labels():

    Clustering with Flair (#2573 #2619)

    Flair now supports clustering by ways of sklearn. Embed your sentences with a pre-trained embedding like below, then cluster then with any algorithm. Check the example below where we use sentence transformers and k-means clustering. A 'trained' clustering model can be saved and loaded for prediction, just like and other Flair classifier:

    from sklearn.cluster import KMeans
    from flair.data import Sentence
    from flair.datasets import TREC_6
    from flair.embeddings import SentenceTransformerDocumentEmbeddings
    from flair.models import ClusteringModel
    embeddings = SentenceTransformerDocumentEmbeddings()
    # store all embeddings in memory which is required to perform clustering
    corpus = TREC_6(memory_mode='full').downsample(0.05)
    clustering_model = ClusteringModel(model=KMeans(n_clusters=6), embeddings=embeddings)
    # fit the model on a corpus
    # save the model
    # load saved clustering model
    model = ClusteringModel.load(model_file="clustering_model.pt")
    # make example sentence
    sentence = Sentence('Getting error in manage categories - not found for attribute "navigation _ column"')
    # predict for sentence
    # print sentence with prediction

    Dataset Manipulations

    You can now change label names, ignore labels and add custom preprocessing when loading a dataset.

    For instance, the standard WNUT_17 dataset comes with 7 NER labels:

    corpus = WNUT_17(in_memory=False)

    which prints:

    Dictionary with 7 tags: <unk>, person, location, group, corporation, product, creative-work

    With the following code, you rename some labels ('person' is renamed to 'PER'), merge 2 labels into 1 ('group' and 'corporation' are merged into 'LOC'), and ignore 2 other labels ('creative-work' and 'product' are ignored):

    corpus = WNUT_17(in_memory=False, label_name_map={
        'person': 'PER',
        'location': 'LOC',
        'group': 'ORG',
        'corporation': 'ORG',
        'product': 'O',
        'creative-work': 'O', # by renaming to 'O' this tag gets ignored

    which prints:

    Dictionary with 4 tags: <unk>, PER, LOC, ORG

    You can manipulate the data even more with custom preprocessing functions. See the example in #2708.

    Other New Features and Data Sets

    • A new WordTagger class for simple word-level predictions (#2607)
    • Classic WordEmbeddings can now be fine-tuned in Flair (#2491) by setting fine_tune=True. Also adds fine-tuning mode of https://arxiv.org/abs/2110.02861 which seem to "reduce gradient variance that comes from the highly non-uniform distribution of input tokens"
    • Add NER_MULTI_CONER Dataset (#2507)
    • Add support for HIPE 2022 (#2675)
    • Allow trainer to work with mutliple learning rates (#2641)
    • Update hyperparameter tuning (#2633)

    Preview Features

    Some preview features in beta stage, use at your own risk.

    Prototypical networks in Flair (#2627)

    Prototype networks learn prototypes for each target class. For each data point to be classified, the network predicts a vector in class-prototype-space, which is then compared to all class prototypes.The prediction is then the closest class prototype. See paper Prototypical Networks for Few-shot Learning for more info.

    @plonerma implemented a custom decoder that can be added to any Flair model that inherits from DefaultClassifier (i.e. early all Flair models). For instance, use this script:

    from flair.data import Corpus
    from flair.datasets import UP_ENGLISH
    from flair.embeddings import TransformerWordEmbeddings
    from flair.models import WordTagger
    from flair.nn import PrototypicalDecoder
    from flair.trainers import ModelTrainer
    # what tag do we want to predict?
    tag_type = 'frame'
    # get a corpus
    corpus: Corpus = UP_ENGLISH().downsample(0.1)
    # make the tag dictionary from the corpus
    tag_dictionary = corpus.make_label_dictionary(label_type=tag_type)
    # initialize simple embeddings
    embeddings = TransformerWordEmbeddings(model="distilbert-base-uncased",
    # initialize prototype decoder
    decoder = PrototypicalDecoder(num_prototypes=len(tag_dictionary),
    # initialize the WordTagger, but pass the prototype decoder
    tagger = WordTagger(embeddings,
    # initialize trainer
    trainer = ModelTrainer(tagger, corpus)
    # run training

    Other Beta features

    • Dependency Parsing in Flair (#2486 #2579)
    • Lemmatization in Flair (#2531)
    • Initial implementation of JsonCorpora and Datasets (#2653)

    Major Refactorings

    With Flair expanding to many new NLP tasks (relation extraction, entity linking, etc.) and model types, we made a number of refactorings to reduce redundancy and make it easier to extend Flair.

    Major refactoring of Label Logic in Flair (#2607 #2609 #2645)

    The labeling logic was growing too complex to accommodate new tasks. With this release, we refactored this logic such that complex label classes like SpanLabel, RelationLabel etc. are removed in favor of a single Label class for all types of label. The Sentence object will now be automatically aware of all labels added to it.

    To illustrate the difference, consider a before-and-after of how to add an entity label to a sentence.


    # example sentence
    sentence = Sentence("Humboldt Universität zu Berlin is located in Berlin .")
    # create span for "Humboldt Universität zu Berlin"
    span = Span(sentence[0:4])
    # make a Span-label
    span_label = SpanLabel(span=span, value='University')
    # add Span-label to sentence
    sentence.add_complex_label(typename='ner',  label=span_label)


    # example sentence
    sentence = Sentence("Humboldt Universität zu Berlin is located in Berlin .")
    # directly add a label to the span "Humboldt Universität zu Berlin"
    sentence[0:4].add_label("ner", "Organization")

    So you can now just get a span from the sentence and add a label to it directly. It will get registered on the sentence as well.

    Refactoring of printouts (#2704)

    We changed and unified printouts across all Flair data points and labels, and updated the documentation to reflect this. Printouts should hopefully now be more concise. Let us know what you think.

    Unified classes to reduce redundancy

    Next to too many Label classes (see above), we also had too many corpora that essentially do the same thing, two partially overlapping transformer embedding classes and too much redundancy in our tokenization classes. This release makes many refactorings to make the code more maintainable:

    • Unify Corpora (#2607): Unifies several corpora into a single object. Before, we had ColumnCorpus, UniversalDependenciesCorpus, CoNNLuCorpus, and EntityLinkingCorpus, which resulted in too much redundancy. Now, there is only the ColumnCorpus for all such datasets
    • Unify Transformer Embeddings (#2558, #2584, #2586): There was too much redundancy and inconsistency between the two Transformer-based embeddings classes TransformerWordEmbedding and TransformerDocumentEmbedding. Thanks to @helpmefindaname, they now both inherit from the same base object and now share all features.
    • Unify Tokenizers (#2607) : The Tokenizer classes no longer return lists of Token, rather lists of strings that the Sentence object converts to tokens, centralizing the offset and whitespace_after detection in one place.

    Simplifications to DefaultClassifier

    The DefaultClassifier is the base class for nearly all models in Flair. With this release, we make a number of simplifications to reduce redundancy across classes and make it more modular.

    • forward_pass simplified to return 3 instead of 4 arguments
    • forward_pass returns embeddings instead of logits allowing us to easily switch out the decoder (see Beta feature on Prototype Networks below)
    • removed the unintuitive spawn logic we no longer need due to Label refactoring
    • unify dropouts across all classes (#2669)

    Sequence tagger refactoring (#2361 #2550, #2561,#2564, #2585, #2565)

    Major refactoring of SequenceTagger for better modularity and code readability.

    Refactoring of Span Logic (#2607 #2609 #2645)

    Spans are no longer stored as word-level 'bioes' tags, but rather directly stored as span-level annotations. The SequenceTagger will still internally use BIO/BIOES tags, but the corpora and sentences no longer explicitly store this information.

    So you now choose the labeling format when instantiating the SequenceTagger, i.e.:

        tagger = SequenceTagger(
            tag_format="BIOES", # choose if you want to use BIOES or BIO internally

    Internally, this refactoring makes a number of changes and simplifications:

    • a number of fields have been added or moved up to the DataPoint class, for convenience, including properties to get start_position and end_position of datapoints, their text, their tag and score (if they have only one tag) and an unlabeled_identifier
    • moves up set_embedding() and to() from the data point classes (Sentence, Token, etc.) to their parent DataPoint
    • a number of methods like get_tag and add_tag have been removed from Token in favor of the get_label and add_label method of the parent DataPoint class
    • The ColumnCorpus will automatically identify which columns are span labels and treat them accordingly

    Code Quality Checks (#2611)

    They are back and more strict than ever! Thanks to @helpmefindaname, we now include mypy and formatting tests as part of our build process, which lead to many changes in the code and a much greater chance at catching errors early.

    Speed and Memory Improvements:

    • EntityLinker class refactored for speed (#2607)
    • Performance improvements in standard evaluate() method, especially for large datasets (#2607)
    • ColumnCorpus no longer does disk reads when in_memory=False, it simply stores the raw data in memory leading to significant speed-ups on large datasets (#2607)
    • Memory management improvements for embeddings (#2645)
    • Efficiency improvements for WordEmbeddings (#2491) and OneHotEmbeddings (#2490)

    Bug Fixes and Improvements

    • Add equality method to Dictionary (#2532)
    • Fix encoding error in lemmatizer (#2539)
    • Fixed printing and logging inconsistencies. (#2665)
    • Readme (#2525 #2618 #2617 #2662)
    • Fix bug in WSD_UFSAC corpus (#2521)
    • change position of model saving in between epochs (#2548)
    • Fix loss weights in TextPairClassifier and RelationExtractor models (#2576)
    • Fix token positions on column corpus (#2440)
    • long sequence transformers of any kind (#2599)
    • The deprecated data_fetcher is finally removed (#2607)
    • Small lm training improvements (#2590)
    • Remove minor bug in NEL_ENGLISH_AIDA corpus (#2615)
    • Fix module import bug (#2616)
    • Fix reloading fast tokenizers (#2622)
    • Fix two small bugs (#2634)
    • Fix .pre-commit-config.yaml (#2651)
    • patch the missing document_delmiter for lm.get_state() (#2658)
    • DocumentPoolEmbeddings class can now be instantiated only with a single embedding (#2645)
    • You can now specify a min_count when computing the label dictionary. Labels below that count will be UNK'ed. (e.g. tag_dictionary = corpus.make_label_dictionary("ner", min_count=10)) (#2607)
    • The Dictionary will now compute count statistics for labels in a corpus (#2607)
    • The ColumnCorpus can now handle relation annotation, dependency tree information and UD feats and misc (#2607)
    • Embeddings are stored as a torch Embedding instead of a gensim keyedvector. That way it will never come to version issues, if gensim doesn't ensure backwards compatibility
    • Make transformer offset calculation more robust (#2714)
    Source code(tar.gz)
    Source code(zip)
  • v0.10(Nov 18, 2021)

    This release adds several new features such as in-built "model cards" for all Flair models, the first pre-trained models for Relation Extraction, better support for fine-tuning and a refactoring of the model training methods for more flexibility. It also fixes a number of critical bugs that were introduced by the refactorings in Flair 0.9.

    Model Trainer Enhancements

    Breaking change: We changed the ModelTrainer such that you now no longer pass the optimizer during initialization. Rather, it is now passed as a parameter of the train or fine_tune method.

    Old syntax:

    # 1. initialize trainer with AdamW optimizer
    trainer = ModelTrainer(classifier, corpus, optimizer=torch.optim.AdamW)
    # 2. run training with small learning rate and mini-batch size

    New syntax (optimizer is parameter of train method):

    # 1. initialize trainer 
    trainer = ModelTrainer(classifier, corpus)
    # 2. run training with AdamW, small learning rate and mini-batch size

    Convenience function for fine-tuning (#2439)

    Adds a fine_tune routine that sets default parameters used for fine-tuning (AdamW optimizer, small learning rate, few epochs, cyclic learning rate scheduling, etc.). Uses the new linear scheduler with warmup (#2415).

    New syntax with fine_tune method:

    from flair.data import Corpus
    from flair.datasets import TREC_6
    from flair.embeddings import TransformerDocumentEmbeddings
    from flair.models import TextClassifier
    from flair.trainers import ModelTrainer
    # 1. get the corpus
    corpus: Corpus = TREC_6()
    # 2. what label do we want to predict?
    label_type = 'question_class'
    # 3. create the label dictionary
    label_dict = corpus.make_label_dictionary(label_type=label_type)
    # 4. initialize transformer document embeddings (many models are available)
    document_embeddings = TransformerDocumentEmbeddings('distilbert-base-uncased', fine_tune=True)
    # 5. create the text classifier
    classifier = TextClassifier(document_embeddings, label_dictionary=label_dict, label_type=label_type)
    # 6. initialize trainer
    trainer = ModelTrainer(classifier, corpus)
    # 7. run training with fine-tuning

    Model Cards (#2457)

    When you train any Flair model, a "model card" will now automatically be saved that stores all training parameters and versions used to train this model. Later when you load a Flair model, you can print the model card and understand how the model was trained.

    The following example trains a small POS-tagger and prints the model card in the end:

    # initialize corpus and make label dictionary for POS tags
    corpus = UD_ENGLISH().downsample(0.01)
    tag_type = "pos"
    tag_dictionary = corpus.make_label_dictionary(tag_type)
    # simple sequence tagger
    tagger = SequenceTagger(hidden_size=256,
    # initialize model trainer and experiment path
    trainer = ModelTrainer(tagger, corpus)
    path = f'resources/taggers/model-card'
    # train for a few epochs
    # load best model and print "model card"
    trained_model = SequenceTagger.load(path + '/best-model.pt')

    This should print a model card like:

    --------- Flair Model Card ---------
    - this Flair model was trained with:
    -- Flair version 0.9
    -- PyTorch version 1.7.1
    -- Transformers version 4.8.1
    ------- Training Parameters: -------
    -- base_path = resources/taggers/model-card
    -- learning_rate = 0.1
    -- mini_batch_size = 32
    -- mini_batch_chunk_size = None
    -- max_epochs = 20
    -- train_with_dev = False
    -- train_with_test = False
    [... shortened ...]

    Resume training any model (#2457)

    Previously, we distinguished between checkpoints and model files. Now all models can function as checkpoints, meaning you can load them and continue training them. Say you want to load the model above (trained to epoch 20) and continue training it to epoch 25. Do it like this:

    # resume training best model, but this time until epoch 25
                   base_path=path + '-resume',

    Pass optimizer and scheduler instance

    You can also now pass an initialized optimizer and scheduler to the train and fine_tune methods.

    Multi-Label Predictions and Confidence Threshold in TARS models (#2430)

    Adding the possibility to set confidence thresholds on multi-label prediction in TARS, and setting whether a problem is single-label or multi-label:

    from flair.models import TARSClassifier
    from flair.data import Sentence
    # 1. Load our pre-trained TARS model for English
    tars: TARSClassifier = TARSClassifier.load('tars-base')
    # switch to a multi-label task (emotion detection)
    # sentence with two emotions
    sentence = Sentence("I am happy and sad")
    # predict normally
    # predict with lower label threshold (you can set this to 0. to get all labels)
    tars.predict(sentence, label_threshold=0.01)
    # predict and enforce a single-label prediction
    tars.predict(sentence, label_threshold=0.01, multi_label=False)

    Relation Extraction ( #2471 #2492)

    We refactored the RelationExtractor for more options, hopefully better code clarity and small speed improvements.

    We also added two few relation extraction models, trained over a modified version of TACRED: relations and relations-fast. To use these models, you also need an entity tagger. The tagger identifies entities, then the relation extractor possible entities.

    For instance use this code:

    from flair.data import Sentence
    from flair.models import RelationExtractor, SequenceTagger
    # 1. make example sentence
    sentence = Sentence("George was born in Washington")
    # 2. load entity tagger and predict entities
    tagger = SequenceTagger.load('ner-fast')
    # check which entities have been found in the sentence
    entities = sentence.get_labels('ner')
    for entity in entities:
    # 3. load relation extractor
    extractor: RelationExtractor = RelationExtractor.load('relations-fast')
    # predict relations
    # check which relations have been found
    relations = sentence.get_labels('relation')
    for relation in relations:


    • Refactoring of WordEmbeddings to avoid gensim version issues and enable further fine-tuning of pre-trained embeddings (#2491)
    • Refactoring of OneHotEmbeddings to fix errors caused by some corpora and enable "stable embeddings" (#2490 )

    Other Enhancements and Bug Fixes

    • Compatibility with gensim 4 and Python 3.9 (#2496)
    • Fix TransformerWordEmbeddings if model_max_length not set in Tokenizer (#2502)
    • Fix TransformerWordEmbeddings handling of lang ids (#2417)
    • Fix attention mask for special Transformer architectures (#2485)
    • Fix regression model (#2424)
    • Fix problems caused by refactoring of Dictionary (#2429 #2435 #2453)
    • Fix infinite loop in Span::to_original_text (#2462)
    • Fix result object in ModelTrainer (#2519)
    • Fix bug in wsd_ufsac corpus (#2521)
    • Fix bugs in TARS and simple sequence tagger (#2468)
    • Add Amharic FLAIR EMBEDDING model (#2494)
    • Add MultiCoNer Dataset (#2507)
    • Add Korean Flair Tutorials (#2516 #2517)
    • Remove hyperparameter features (#2518)
    • Make it optional to create logfiles and loss files (#2421)
    • Small simplification of TransformerWordEmbeddings (#2425)
    Source code(tar.gz)
    Source code(zip)
  • v0.9(Aug 29, 2021)

    With release 0.9 we are refactoring Flair for simplicity and speed, to make Flair faster and more easily scale to new NLP tasks. The first new tasks included in this release are Relation Extraction (RE), support for GLUE benchmark tasks and Entity Linking - all in beta for early adopters! We're working towards a Flair 1.0 release that will span the whole suite of standard NLP tasks. Also included is a new approach for Zero-Shot Sequence Labeling based on TARS! This release also includes a wealth of new datasets for all these tasks and tons of other new features and bug fixes.

    Zero-Shot Sequence Labeling with TARS (#2260)

    We extend the TARS zero-shot learning approach to sequence labeling and ship a pre-trained model for English NER. Try defining some classes and see if the model can find them:

    # 1. Load zero-shot NER tagger
    tars = TARSTagger.load('tars-ner')
    # 2. Prepare some test sentences
    sentences = [
        Sentence("The Humboldt University of Berlin is situated near the Spree in Berlin, Germany"),
        Sentence("Bayern Munich played against Real Madrid"),
        Sentence("I flew with an Airbus A380 to Peru to pick up my Porsche Cayenne"),
        Sentence("Game of Thrones is my favorite series"),
    # 3. Define some classes of named entities such as "soccer teams", "TV shows" and "rivers"
    labels = ["Soccer Team", "University", "Vehicle", "River", "City", "Country", "Person", "Movie", "TV Show"]
    tars.add_and_switch_to_new_task('task 1', labels, label_type='ner')
    # 4. Predict for these classes and print results
    for sentence in sentences:

    This should print:

    The Humboldt <B-University> University <I-University> of <I-University> Berlin <E-University> is situated near the Spree <S-River> in Berlin <S-City> , Germany <S-Country>
    Bayern <B-Soccer Team> Munich <E-Soccer Team> played against Real <B-Soccer Team> Madrid <E-Soccer Team>
    I flew with an Airbus <B-Vehicle> A380 <E-Vehicle> to Peru <S-City> to pick up my Porsche <B-Vehicle> Cayenne <E-Vehicle>
    Game <B-TV Show> of <I-TV Show> Thrones <E-TV Show> is my favorite series

    So in these examples, we are finding entity classes such as "TV show" (Game of Thrones), "vehicle" (Airbus A380 and Porsche Cayenne), "soccer team" (Bayern Munich and Real Madrid) and "river" (Spree), even though the model was never explicitly trained for this. Note that this is ongoing research and the examples are a bit cherry-picked. We expect the zero-shot model to improve quite a bit until the next release.

    New NLP Tasks and Datasets

    We prototypically now support new tasks such as GLUE benchmark, Relation Extraction and Entity Linking. With this, we ship the datasets and model classes you need to train your own models. But we are still tweaking both methods, meaning that we don't ship any pre-trained models as-of-yet.

    GLUE Benchmark (#2149 #2363)

    A standard benchmark to evaluate progress in language understanding, mostly consisting of single and pairwise sentence classification tasks.

    New datasets in Flair:

    • 'GLUE_COLA' - The Corpus of Linguistic Acceptability from GLUE benchmark
    • 'GLUE_MNLI' - The Multi-Genre Natural Language Inference Corpus from the GLUE benchmark
    • 'GLUE_RTE' - The RTE task from the GLUE benchmark
    • 'GLUE_QNLI' - The Stanford Question Answering Dataset formated as NLI task from the GLUE benchmark
    • 'GLUE_WNLI' - The Winograd Schema Challenge formated as NLI task from the GLUE benchmark
    • 'GLUE_MRPC' - The MRPC task from GLUE benchmark
    • 'GLUE_QQP' - The Quora Question Pairs dataset where the task is to determine whether a pair of questions are semantically equivalent

    Initialize datasets like so:

    from flair.datasets import GLUE_QNLI
    # load corpus
    corpus = GLUE_QNLI()
    # print corpus
    # print first sentence-pair of training data split
    # print all labels in corpus

    Relation Extraction (#2333 #2352)

    Relation extraction classifies if and which relationship holds between two entities in a text.

    Model class: RelationExtractor

    Datasets in Flair:

    • 'RE_ENGLISH_CONLL04' - the CoNLL-04 Relation Extraction dataset (#2333)
    • 'RE_ENGLISH_SEMEVAL2010' - the SemEval-2010 Task 8 dataset on Multi-Way Classification of Semantic Relations Between Pairs of Nominals (#2333)
    • 'RE_ENGLISH_TACRED' - the TAC Relation Extraction Dataset](https://nlp.stanford.edu/projects/tacred/) with 41 relations (download required) (#2333)
    • 'RE_ENGLISH_DRUGPROT' - the DrugProt corpus from Biocreative VII Track 1 on drug and chemical-protein interactions (#2340 #2352)

    Initialize datasets like so:

    # initalize CoNLL 04 corpus for Relation extraction
    corpus = RE_ENGLISH_CONLL04()
    # print first sentence of training split with annotations
    sentence = corpus.train[0]
    # print label dictionary
    label_dict = corpus.make_label_dictionary("relation")

    Entity Linking (#2375)

    Entity Linking goes one step further than NER and uniquely links entities to knowledge bases such as Wikipedia.

    Model class: EntityLinker

    Datasets in Flair:

    from flair.datasets import NEL_ENGLISH_REDDIT
    # load corpus
    corpus = NEL_ENGLISH_REDDIT()
    # print corpus
    # print a sentence of training data split

    New NER Datasets

    Other datasets

    • 'YAHOO_ANSWERS' - The 10 largest main categories from the Yahoo! Answers (#2198)
    • Various Universal Dependencies datasets (#2211, #2216, #2219, #2221, #2244, #2245, #2246, #2247, #2223, #2248, #2235, #2236, #2239, #2226)

    New Functionality

    Support for Arabic NER (#2188)

    Flair now supports NER and POS tagging for Arabic. To tag an Arabic sentence, just load the appropriate model:

    # load model
    tagger = SequenceTagger.load('ar-ner')
    # make Arabic sentence
    sentence = Sentence("احب برلين")
    # predict NER tags
    # print sentence with predicted tags
    for entity in sentence.get_labels('ner'):

    This should print:

    LOC [برلين (2)] (0.9803) 

    More flexibility on main metric (#2161)

    When training models, you can now chose any standard evaluation metric for model selection (previously it was fixed to micro F1). When calling the trainer, simply pass the desired metric as main_evaluation_metric like so:

                  main_evaluation_metric=("macro avg", 'f1-score'),

    In this example, we now use macro F1 instead of the default micro F1.

    Add handling for mapping labels to 'O' #2254

    In ColumnDataset, labels can be remapped to other labels. But sometimes you may not wish to use all label types in a given dataset. You can now remap them to 'O' and so exclude them.

    For instance, to load CoNLL-03 without MISC, do:

    corpus = CONLL_03(
        label_name_map={'MISC': 'O'}


    • add per-label thresholds for prediction (#2366)

    • add support for Spanish clinical Flair embeddings (#2323)

    • added 'mean', 'max' pooling strategy for TransformerDocumentEmbeddings class (#2180)

    • new DocumentCNNEmbeddings class to embed text with a trainable CNN (#2141)

    • allow negative examples in ClassificationCorpus (#2233)

    • added new parameter to save model each k epochs during training (#2146)

    • log epoch of best model instead of printing it during training (#2286)

    • add option to exclude specific sentences from dataset (#2262)

    • improved tensorboard logging (#2164)

    • return predictions during evaluation (#2162)

    Internal Refactorings

    Refactor for simplicity and extensibility (#2333 #2351 #2356 #2377 #2379 #2382 #2184)

    In order to accommodate all these new NLP task types (plus many more in the pipeline), we restructure the flair.nn.Model class such that most models now inherit from DefaultClassifier. This removes many redundancies as most models do classification and are really only different in what they classify and how they apply embeddings. Models that inherit from DefaultClassifier need only implement the method forward_pass, making each model class only a few lines of code.

    Check for instance our implementation of the RelationExtractor class to see how easy it now is to add a new tasks!

    Refactor for speed

    • Flair models trained with transformers (such as the FLERT models) were previously not making use of mini-batching, greatly slowing down training and application of such models. We refactored the TransformerWordEmbeddings class, yielding significant speed-ups depending on the mini-batch size used. We observed speed-ups from x2 to x6. (#2385 #2389 #2384)

    • Improve training speed of Flair embeddings (#2203)

    Bug fixes & improvements

    • fixed references to multi-X-fast Flair embedding models (#2150)
    • fixed serialization of DocumentRNNEmbeddings (#2155)
    • fixed separator in cross-attention mode (#2156)
    • fixed ID for Slovene word embeddings in the doc (#2166)
    • close log_handler after training is complete. (#2170)
    • fixed bug in IMDB dataset (#2172)
    • fixed IMDB data splitting logic (#2175)
    • fixed XLNet and Transformer-XL Execution (#2191)
    • remove unk token from Ner labeling (#2225)
    • fxed typo in property name (#2267)
    • fixed typos (#2303 #2373)
    • fixed parallel corpus (#2306)
    • fixed SegtokSentenceSplitter Incorrect Sentence Position Attributes (#2312)
    • fixed loading of old serialized models (#2322)
    • updated url for BioSemantics corpus (#2327)
    • updated requirements (#2346)
    • serialize multi_label_threshold for classification models (#2368)
    • small refactorings in ModelTrainer (#2184)
    • moving Path construction of flair.cache_root (#2241)
    • documentation improvement (#2304)
    • add model fit tests #2378
    Source code(tar.gz)
    Source code(zip)
  • v0.8(Mar 5, 2021)

    Release 0.8 adds major new features to Flair, including our best named entity recognition (NER) models yet and the ability to host, share and test Flair models on the HuggingFace model hub! In addition, there is a host of improvements, new features and new datasets to check out!

    FLERT (#2031 #2032 #2104)

    This release adds the "FLERT" approach to train sequence tagging models using cross-sentence features as presented in our recent paper. This yields new state-of-the-art models which we include in Flair, as well as the features to easily train your own "FLERT" models.

    Pre-trained FLERT models (#2130)

    We add 5 new NER models for English (4-class and 18-class), German, Dutch and Spanish (4-class each). Load for instance with:

    from flair.data import Sentence
    from flair.models import SequenceTagger
    # load tagger
    tagger = SequenceTagger.load("ner-large")
    # make example sentence
    sentence = Sentence("George Washington went to Washington")
    # predict NER tags
    # print sentence
    # print predicted NER spans
    print('The following NER tags are found:')
    # iterate over entities and print
    for entity in sentence.get_spans('ner'):

    If you want to test these models in action, for instance the new large English Ontonotes model with 18 classes, you can now use the hosted inference API on the HF model hub, like here.

    Contextualized Sentences

    In order to enable cross-sentence context, we made some changes to the Sentence object and data readers:

    1. Sentence objects now have next_sentence() and previous_sentence() methods that are set automatically if loaded through ColumnCorpus. This is a pointer system to navigate through sentences in a corpus:
    # load corpus
    corpus = MIT_MOVIE_NER_SIMPLE(in_memory=False)
    # get a sentence
    sentence = corpus.test[123]
    # get the previous sentence
    # get the sentence after that
    # get the sentence after the next sentence

    This allows dynamic computation of contexts in the embedding classes.

    1. Sentence objects now have the is_document_boundary field which is set through the ColumnCorpus. In some datasets, there are sentences like "-DOCSTART-" that just indicate document boundaries. This is now recorded as a boolean in the object.

    Refactored TransformerWordEmbeddings (breaking)

    TransformerWordEmbeddings refactored for dynamic context, robustness to long sentences and readability. The names of some constructor arguments have changed for clarity: pooling_operation is now subtoken_pooling (to make clear that we pool subtokens), use_scalar_mean is now layer_mean (we only do a simple layer mean) and use_context can now optionally take an integer to indicate the length of the context. Default arguments are also changed.

    For instance, to create embeddings with a document-level context of 64 subtokens, init like this:

    embeddings = TransformerWordEmbeddings(

    Train your Own FLERT Models

    You can train a FLERT-model like this:

    import torch
    from flair.data import Sentence
    from flair.datasets import CONLL_03, WNUT_17
    from flair.embeddings import TransformerWordEmbeddings, DocumentPoolEmbeddings, WordEmbeddings
    from flair.models import SequenceTagger
    from flair.trainers import ModelTrainer
    corpus = CONLL_03()
    use_context = 64
    hf_model = 'xlm-roberta-large'
    embeddings = TransformerWordEmbeddings(
    tag_dictionary = corpus.make_tag_dictionary('ner')
    # init bare-bones tagger (no reprojection, LSTM or CRF)
    tagger: SequenceTagger = SequenceTagger(
    # train with XLM parameters (AdamW, 20 epochs, small LR)
    trainer = ModelTrainer(tagger, corpus, optimizer=torch.optim.AdamW)
    from torch.optim.lr_scheduler import OneCycleLR
    context_string = '+context' if use_context else ''

    We recommend training FLERT this way if accuracy is by far the most important feature you need. FLERT is quite slow since it works on the document-level.

    HuggingFace model hub integration (#2040 #2108 #2115)

    We now host Flair sequence tagging models on the HF model hub (thanks for all the support @HuggingFace!).

    Overview of all models. There is a dedicated 'Flair' tag on the hub, so to get a list of all Flair models, check here.

    The hub allows all users to upload and share their own models. Even better, you can enable the Inference API and so test all models online without downloading and running them. For instance, you can test our new very powerful English 18-class NER model here.

    To load any sequence tagger on the model hub, use the string identifier when instantiating a model. For instance, to load our English ontonotes model with the id "flair/ner-english-ontonotes-large", do

    from flair.data import Sentence
    from flair.models import SequenceTagger
    # load tagger
    tagger = SequenceTagger.load("flair/ner-english-ontonotes-large")
    # make example sentence
    sentence = Sentence("On September 1st George won 1 dollar while watching Game of Thrones.")
    # predict NER tags
    # print sentence
    # print predicted NER spans
    print('The following NER tags are found:')
    # iterate over entities and print
    for entity in sentence.get_spans('ner'):

    Other New Features

    New Task: Recognizing Textual Entailment (#2123)

    Thanks to @marcelmmm we now support training textual entailment tasks (in fact, all pairwise sentence classification tasks) in Flair.

    For instance, if you want to train an RTE task of the GLUE benchmark use this script:

    import torch
    from flair.data import Corpus
    from flair.datasets import GLUE_RTE
    from flair.embeddings import TransformerDocumentEmbeddings
    # 1. get the entailment corpus
    corpus: Corpus = GLUE_RTE()
    # 2. make the tag dictionary from the corpus
    label_dictionary = corpus.make_label_dictionary()
    # 3. initialize text pair tagger
    from flair.models import TextPairClassifier
    tagger = TextPairClassifier(
    # 4. train trainer with AdamW
    from flair.trainers import ModelTrainer
    trainer = ModelTrainer(tagger, corpus, optimizer=torch.optim.AdamW)
    # 5. run training
                  mini_batch_chunk_size=2, # this can be removed if you hae a big GPU

    Add possibility to specify empty label name to CSV corpora (#2068)

    Some CSV classification datasets contain a value that means "no class". We now extend the CSVClassificationDataset so that it is possible to specify which value should be skipped using the no_class_label argument.

    For instance:

    # load corpus
    corpus = CSVClassificationCorpus(
        column_name_map={3: 'text', 4: 'label', 5: 'label', 6: 'label', 7: 'label', 8: 'label', 9: 'label'},

    This causes all entries of NONE in one of the label columns to be skipped.

    More options for splits in corpora and training (#2034)

    For various reasons, we might want to have a Corpus that does not define all three splits (train/dev/test). For instance, we might want to train a model over the entire dataset and not hold out any data for validation/evaluation.

    We add several ways of doing so.

    1. If a dataset has predefined splits, like most NLP datasets, you can pass the arguments train_with_test and train_with_dev to the ModelTrainer. This causes the trainer to train over all three splits (and do no evaluation):
    1. You can also now create a Corpus with fewer splits without having all three splits automatically sampled. Pass sample_missing_splits=False as argument to do this. For instance, to load SemCor WSD corpus only as training data, do:
    semcor = WSD_UFSAC(train_file='semcor.xml', sample_missing_splits=False, autofind_splits=False)

    Add TFIDF Embeddings (#2086)

    We added some old-school embeddings (thanks @yosipk), namely the legendary TF-IDF document embeddings. These are often good baselines, and additionally they keep NLP veterans nostalgic, if not happy.

    To initialize these embeddings, you must pass the train split of your training corpus, i.e.

    embeddings = DocumentTFIDFEmbeddings(corpus.train, max_features=10000)

    This triggers the process where the most common words are used to featurize documents.

    New Datasets

    Hungarian NER Corpus (#2045)

    Added the Hungarian business news corpus annotated with NER information (thanks to @alibektas).

    # load Hungarian business NER corpus
    corpus = BUSINESS_HUN()

    StackOverflow NER Corpus (#2052)

    # load StackOverflow business NER corpus
    corpus = STACKOVERFLOW_NER()

    Added GermEval 18 Offensive Language dataset (#2102)

    # load StackOverflow business NER corpus

    Added RTE corpora of GLUE and SuperGLUE

    # load the recognizing textual entailment corpus of the GLUE benchmark
    corpus = GLUE_RTE()


    Allow newlines as Tokens in a Sentence (#2070)

    Newlines and tabs can now become Tokens in a Sentence:

    # make sentence with newlines and tabs
    sentence: Sentence = Sentence(["I", "\t", "ich", "\n", "you", "\t", "du", "\n"], use_tokenizer=True)
    # Alternatively: sentence: Sentence = Sentence("I \t ich \n you \t du \n", use_tokenizer=False)
    # print sentence and each token
    for token in sentence:

    Improve transformer serialization (#2046)

    We improved the serialization of the TransformerWordEmbeddings class such that you can now train a model with one version of the transformers library and load it with another version. Previously, if you trained a model with transformers 3.5.1 and loaded it with 3.1.01, or trained with 3.5.1 and loaded with 4.1.1, or other version mismatches, there would either be errors or bad predictions.

    Migration guide: If you have a model trained with an older version of Flair that uses TransformerWordEmbeddings you can save it in the new version-independent format by loading the model with the same transformers version you used to train it, and then saving it again. The newly saved model is then version-independent:

    # load old model, but use the *same transformer version you used when training this model*
    tagger = SequenceTagger.load('path/to/old-model.pt')
    # save the model. It is now version-independent and can for instance be loaded with transformers 4.

    Fix regression prediction errors (#2067)

    Fix of two problems in the regression model:

    • the predict() method was unable to set labels and threw errors (see #2056)
    • predicted labels had no label name

    Now, you can set a label name either in the predict method or during instantiation of the regression model you want to train. So the full code for training a regression model and using it to predict is:

    # load regression dataset
    corpus = WASSA_JOY()
    # make simple document embeddings
    embeddings = DocumentPoolEmbeddings([WordEmbeddings('glove')], fine_tune_mode='linear')
    # init model and give name to label
    model = TextRegressor(embeddings, label_name='happiness')
    # target folder
    output_folder = 'resources/taggers/regression_test/'
    # run training
    trainer = ModelTrainer(model, corpus)
    # load model
    model = TextRegressor.load(output_folder + 'best-model.pt')
    # predict for sentence
    sentence = Sentence('I am so happy')
    # print sentence and prediction

    In my example run, this prints the following sentence + predicted value:

    Sentence: "I am so happy"   [− Tokens: 4  − Sentence-Labels: {'happiness': [0.9239126443862915 (1.0)]}]

    Do not shuffle first epoch during training (#2058)

    Normally, we shuffle sentences at each epoch during training in the ModelTrainer class. However, in some cases it makes sense to see sentences in their natural order during the first epoch, and shuffle only from the second epoch onward.

    Bug Fixes and Improvements

    • Update to transformers 4 (#2057)
    • Fix the evaluate() method in the SimilarityLearner class (#2113)
    • Fix memory memory leak in WordEmbeddings (#2018)
    • Add support for Transformer-XL Embeddings (#2009)
    • Restrict numpy version to <1.20 for Python 3.6 (#2014)
    • Small formatting and variable declaration changes (#2022)
    • Fix document boundary offsets for Dutch CoNLL-03 (#2061)
    • Changed the torch version in requirements.txt: Torch>=1.5.0 (#2063)
    • Fix linear input dimension if the reproject (#2073)
    • Various improvements for TARS (#2090 #2128)
    • Added a link to the interpret-flair repo (#2096)
    • Improve documentatin ( #2110)
    • Update sentencepiece and gdown version (#2131)
    • Add to_plain_string method to Span class (#2091)
    Source code(tar.gz)
    Source code(zip)
  • v0.7(Dec 1, 2020)

    Release 0.7 adds major few-shot and zero-shot learning capabilities to Flair with our new TARS approach, plus support for the Universal Proposition Banks, new NER datasets and lots of other new features!

    Few-Shot and Zero-Shot Classification with TARS (#1917 #1926)

    With TARS we add a major new feature to Flair for zero-shot and few-shot classification. Details on the approach can be found in our paper Halder et al. (2020). Our approach allows you to classify text in cases in which you have little or even no training data at all.

    This example illustrates how you predict new classes without training data:

    # 1. Load our pre-trained TARS model for English
    tars = TARSClassifier.load('tars-base')
    # 2. Prepare a test sentence
    sentence = flair.data.Sentence("I am so glad you liked it!")
    # 3. Define some classes that you want to predict using descriptive names
    classes = ["happy", "sad"]
    #4. Predict for these classes
    tars.predict_zero_shot(sentence, classes)
    # Print sentence with predicted labels

    For a full overview of TARS features, please refer to our new TARS tutorial.

    Other New Features

    Option to set Flair seed (#1979)

    Adds the possibility to set a seed via wrapping the Hugging Face Transformers library helper method (thanks @stefan-it).

    By specifying a seed with:

    import flair

    you can make experimental runs reproducible. The wrapped set_seed method sets seeds for random, numpy and torch. More details here.

    Control multi-word behavior in UD datasets (#1981)

    To better handle multi-words in UD corpora, we introduce the split_multiwords constructor argument to all UD corpora which by default is set to True. It controls the handling of multiwords that are split into different tokens. For instance the German "am" is split into two different tokens: "am" -> "an" + "dem". Or the French "aux" -> "a" + "les".

    If split_multiwords is set to True, they are split as in UD. If set to False, we keep the original multiword as a single token. Example:

    # default mode: multiwords are split
    corpus = UD_GERMAN(split_multiwords=True)
    # print sentence 179
    # alternative mode: multiwords are kept as original
    corpus = UD_GERMAN(split_multiwords=False)
    # print sentence 179

    This prints

    Ein Hotel zu dem Wohlfühlen.
    Ein Hotel zum Wohlfühlen.

    The latter is how it appears in text, the former is after splitting of multiwords.

    Pass pretokenized sentence to Sentence object (#1965)

    You can now pass pass a pretokenized sequence as list of words (thanks @ulf1):

    from flair.data import Sentence
    sentence = Sentence(['The', 'grass', 'is', 'green', '.'])

    This should print:

    Sentence: "The grass is green ."   [− Tokens: 5]

    Map label names in sequence labeling datasets (#1988)

    You can now pass a label map to sequence labeling datasets to change label names (thanks @pharnisch).

    # print tag dictionary with mapped names
    corpus = CONLL_03_DUTCH(label_name_map={'PER': 'person', 'ORG': 'organization', 'LOC': 'location', 'MISC': 'other'})
    # print tag dictionary with original names
    corpus = CONLL_03_DUTCH()

    Data Sets

    Universal Proposition Banks (#1870 #1866 #1888)

    Flair 0.7 adds support 7 Universal Proposition Banks to train your own multilingual semantic role labelers (thanks to @Dabendorf).

    Load for instance with:

    # load English Universal Proposition Bank
    corpus = UP_ENGLISH()
    # make dictionary of frames
    frame_dictionary = corpus.make_tag_dictionary('frame')

    Now available for Finnish, Chinese, Italian, French, German, Spanish and English

    NER Corpora

    We add support for 6 new NER corpora:

    Arabic NER Corpus (#1901)

    Added the ANER corpus for Arabic NER (thanks to @megantosh).

    # load Arabic NER corpus
    corpus = ANER_CORP()

    Movie NER Corpora (#1912)

    Added the MIT movie reviews corpora annotated with NER information, in the simple and complex variant (thanks to @pharnisch).

    # load simple movie NER corpus
    corpus = MITMovieNERSimple()
    # load complex movie NER corpus
    corpus = MITMovieNERComplex()

    Added SEC Fillings NER corpus (#1922)

    Added corpus of SEC fillings annotated with 4-class NER tags (thanks to @samahakk).

    # load SEC fillings corpus
    corpus = SEC_FILLINGS()

    WNUT 2020 NER dataset support (#1942)

    Added corpus of wet lab protocols annotated with NER information used for WNUT 2020 challenge (thanks to @aynetdia).

    # load wet lab protocol data
    corpus = WNUT_2020_NER()

    Weibo NER dataset support (#1944)

    Added dataset about NER for Chinese Social Media (thanks to @87302380).

    # load Weibo NER data
    corpus = WEIBO_NER()

    Added Finnish NER corpus (#1946)

    Added the TURKU corpus for Finnish NER (thanks to @melvelet).

    # load Finnish NER data
    corpus = TURKU_NER()

    Universal Depdency Treebanks

    We add support for 11 new UD treebanks:

    • Greek UD Treebank (#1933, thanks @malamasn)
    • Livvi UD Treebank (#1953, thanks @hebecked)
    • Naija UD Treebank (#1952, thanks @teddim420)
    • Buryat UD Treebank (#1954, thanks @MaxDall)
    • North Sami UD Treebank (#1955, thanks @dobbersc)
    • Maltese UD Treebank (#1957, thanks @phkuep)
    • Marathi UD Treebank (#1958, thanks @polarlyset)
    • Afrikaans UD Treebank (#1959, thanks @QueStat)
    • Gothic UD Treebank (#1961, thanks @wjSimon)
    • Old French UD Treebank (#1964, thanks @Weyaaron)
    • Wolof UD Treebank (#1967, thanks @LukasOpp)

    Load each with language name, for instance:

    # load Gothic UD treebank data
    corpus = UD_GOTHIC()

    Added GoEmotions text classification corpus (#1914)

    Added GoEmotions dataset containing 58k Reddit comments labeled with 27 emotion categories. Load with:

    # load GoEmotions corpus
    corpus = GO_EMOTIONS()

    Enhancements and bug fixes

    • Add handling for micro-average precision and recall (#1935)
    • Make dev and test splits in treebanks optional (#1951)
    • Updated communicative functions model (#1857)
    • Biomedical Data: Explicit encodings for Windows Support (#1893)
    • Fix wrong abstract method (#1923 #1940)
    • Improve tutorial (#1939)
    • Fix requirements (#1971 )
    Source code(tar.gz)
    Source code(zip)
  • v0.6.1(Sep 23, 2020)

    Release 0.6.1 is bugfix release that fixes the issues caused by moving the server that originally hosted the Flair models. Additionally, this release adds a ton of new NER datasets, including the XTREME corpus for 40 languages, and a new model for NER on German-language legal text.

    New Model: Legal NER (#1872)

    Add legal NER model for German. Trained using the German legal NER dataset available here that can be loaded in Flair with the LER_GERMAN corpus object.

    Uses German Flair and FastText embeddings and gets 96.35 F1 score.

    Use like this:

    # load German LER tagger
    tagger = SequenceTagger.load('de-ler')
    # example text
    text = "vom 6. August 2020. Alle Beschwerdeführer befinden sich derzeit gemeinsam im Urlaub auf der Insel Mallorca , die vom Robert-Koch-Institut als Risikogebiet eingestuft wird. Sie wollen am 29. August 2020 wieder nach Deutschland einreisen, ohne sich gemäß § 1 Abs. 1 bis Abs. 3 der Verordnung zur Testpflicht von Einreisenden aus Risikogebieten auf das SARS-CoV-2-Virus testen zu lassen. Die Verordnung sei wegen eines Verstoßes der ihr zugrunde liegenden gesetzlichen Ermächtigungsgrundlage, des § 36 Abs. 7 IfSG , gegen Art. 80 Abs. 1 Satz 1 GG verfassungswidrig."
    sentence = Sentence(text)
    # predict and print entities
    for entity in sentence.get_spans('ner'):

    New Datasets

    Add XTREME and WikiANN corpora for multilingual NER (#1862)

    These huge corpora provide training data for NER in 176 languages. You can either load the language-specific parts of it by supplying a language code:

    # load German Xtreme
    german_corpus = XTREME('de')
    # load French Xtreme
    french_corpus = XTREME('fr')

    Or you can load the default 40 languages at once into one huge MultiCorpus by not providing a language ID:

    # load Xtreme MultiCorpus for all
    multi_corpus = XTREME()

    Add Twitter NER Dataset (#1850)

    Dataset of tweets annotated with NER tags. Load with:

    # load twitter dataset
    corpus = TWITTER_NER()
    # print example tweet

    Add German Europarl NER Dataset (#1849)

    Dataset of German-language speeches in the European parliament annotated with standard NER tags like person and location. Load with:

    # load corpus
    corpus = EUROPARL_NER_GERMAN()
    # print first test sentence

    Add MIT Restaurant NER Dataset (#1177)

    Dataset of English restaurant reviews annotated with entities like "dish", "location" and "rating". Load with:

    # load restaurant dataset
    corpus = MIT_RESTAURANTS()
    # print example sentence

    Add Universal Propositions Banks for French and German (#1866)

    Our kickoff into supporting the Universal Proposition Banks adds the first two UP datasets to Flair. Load with:

    # load German UP
    corpus = UP_GERMAN()
    # print example sentence

    Add Universal Dependencies Dataset for Chinese (#1880)

    Adds the Kyoto dataset for Chinese. Load with:

    # load Chinese UD dataset
    corpus = UD_CHINESE_KYOTO()
    # print example sentence

    Bug fixes

    • Move models to HU server (#1834 #1839 #1842)
    • Fix deserialization issues in transformer tokenizers #1865
    • Documentation fixes (#1819 #1821 #1836 #1852)
    • Add link to a repo with examples of Flair on GCP (#1825)
    • Correct variable names (#1875)
    • Fix problem with custom delimiters in ColumnDataset (#1876)
    • Fix offensive language detection model (#1877)
    • Correct Dutch NER model (#1881)
    Source code(tar.gz)
    Source code(zip)
  • v0.6(Aug 17, 2020)

    Release 0.6 is a major biomedical NLP upgrade for Flair, adding state-of-the-art models for biomedical NER, support for 31 biomedical NER corpora, clinical POS tagging, speculation and negation detection in biomedical literature, and many other features such as multi-tagging and one-cycle learning.

    Biomedical Models and Datasets:

    Most of the biomedical models and datasets were developed together with the Knowledge Management in Bioinformatics group at the HU Berlin, in particular @leonweber and @mariosaenger. This page gives an overview of the new models and datasets, and example tutorials. Some highlights:

    Biomedical NER models (#1790)

    Flair now has pre-trained models for biomedical NER trained over unified versions of 31 different biomedical corpora. Because they are trained on so many different datasets, the models are shown to be very robust with new datasets, outperforming all previously available off-the-shelf datasets. If you want to load a model to detect "diseases" in text for instance, do:

    # make a sentence
    sentence = Sentence("Behavioral abnormalities in the Fmr1 KO2 Mouse Model of Fragile X Syndrome")
    # load disease tagger and predict
    tagger = SequenceTagger.load("hunflair-disease")

    Done! Let's print the diseases found by the tagger:

    for entity in sentence.get_spans():

    This should print:

    Span [1,2]: "Behavioral abnormalities"   [− Labels: Disease (0.6736)]
    Span [10,11,12]: "Fragile X Syndrome"   [− Labels: Disease (0.99)]

    You can also get one model that finds 5 biomedical entity types (diseases, genes, species, chemicals and cell lines), like this:

    # load bio-NER tagger and predict
    tagger = MultiTagger.load("hunflair")

    This should print:

    Span [1,2]: "Behavioral abnormalities"   [− Labels: Disease (0.6736)]
    Span [10,11,12]: "Fragile X Syndrome"   [− Labels: Disease (0.99)]
    Span [5]: "Fmr1"   [− Labels: Gene (0.838)]
    Span [7]: "Mouse"   [− Labels: Species (0.9979)]

    So it now also finds genes and species. As explained here these models work best if you use them together with a biomedical tokenizer.

    Biomedical NER datasets (#1790)

    Flair now supports 31 biomedical NER datasets out of the box, both in their standard versions as well as the "Huner" splits for reproducibility of experiments. For a full list of datasets, refer to this page.

    You can load a dataset like this:

    # load one of the bioinformatics corpora
    corpus = JNLPBA()
    # print statistics and one sentence

    We also include "huner" corpora that combine many different biomedical datasets into a single corpus. For instance, if you execute the following line:

    # load combined chemicals corpus
    corpus = HUNER_CHEMICAL()

    This loads a combination of 6 different corpora that contain annotation of chemicals into a single corpus. This allows you to train stronger cross-corpus models since you now combine training data from many sources. See more info here.

    POS model for Portuguese clinical text (#1789)

    Thanks to @LucasFerroHAILab, we now include a model for part-of-speech tagging in Portuguese clinical text. Run this model like this:

    # load your tagger
    tagger = SequenceTagger.load('pt-pos-clinical')
    # example sentence
    sentence = Sentence('O vírus Covid causa fortes dores .')

    You can find more details in their paper here.

    Model for negation and speculation in biomedical literature (#1758)

    Using the BioScope corpus, we trained a model to recognize negation and speculation in biomedical literature. Use it like this:

    sentence = Sentence("The picture most likely reflects airways disease")
    tagger = SequenceTagger.load("negation-speculation")
    for entity in sentence.get_spans():

    This should print:

    Span [4,5,6,7]: "likely reflects airways disease"   [− Labels: SPECULATION (0.9992)]

    Thus indicating that this portion of the sentence is speculation.

    Other New Features:

    MultiTagger (#1791)

    We added support for tagging text with multiple models at the same time. This can save memory usage and increase tagging speed.

    For instance, if you want to POS tag, chunk, NER and detect frames in your text at the same time, do:

    # load tagger for POS, chunking, NER and frame detection
    tagger = MultiTagger.load(['pos', 'upos', 'chunk', 'ner', 'frame'])
    # example sentence
    sentence = Sentence("George Washington was born in Washington")
    # predict

    This will give you a sentence annotated with 5 different layers of annotation.

    Sentence splitting

    Flair now includes convenience methods for sentence splitting. For instance, to use segtok to split and tokenize a text into sentences, use the following code:

    from flair.tokenization import SegtokSentenceSplitter
    # example text with many sentences
    text = "This is a sentence. This is another sentence. I love Berlin."
    # initialize sentence splitter
    splitter = SegtokSentenceSplitter()
    # use splitter to split text into list of sentences
    sentences = splitter.split(text)  

    We also ship other splitters, such as SpacySentenceSplitter (requires SpaCy to be installed).

    Japanese tokenization (#1786)

    Thanks to @himkt we now have expanded support for Japanese tokenization in Flair. For instance, use the following code to tokenize a Japanese sentence without installing extra libraries:

    from flair.data import Sentence
    from flair.tokenization import JapaneseTokenizer
    # init japanese tokenizer
    tokenizer = JapaneseTokenizer("janome")
    # make sentence (and tokenize)
    sentence = Sentence("私はベルリンが好き", use_tokenizer=tokenizer)
    # output tokenized sentence

    One-Cycle Learning (#1776)

    Thanks to @lucaventurini2 Flair one supports one-cycle learning, which may give quicker convergence. For instance, train a model in 20 epochs using the code below:

    # train as always
    trainer = ModelTrainer(tagger, corpus)
    # set one cycle LR as scheduler


    Changes in convention

    Turn on tokenizer by default in Sentence object (#1806)

    The Sentence object now executes tokenization (use_tokenizer=True) by default:

    # Tokenizes by default
    sentence = Sentence("I love Berlin.")
    # i.e. this is equivalent to
    sentence = Sentence("I love Berlin.", use_tokenizer=True)
    # i.e. if you don't want to use tokenization, set it to False
    sentence = Sentence("I love Berlin.", use_tokenizer=False)

    TransformerWordEmbeddings now handle long documents by default

    Previously, so had to set allow_long_sentences=True to enable handling of long sequences (greater than 512 subtokens) in TransformerWordEmbeddings. This is no longer necessary as this value is now set to True by default.

    Bug fixes

    • Fix serialization of BytePairEmbeddings (#1802)
    • Fix issues with loading models that use ELMoEmbeddings (#1803)
    • Allow longer lengths in transformers that can handle more than 512 subtokens (#1804)
    • Fix encoding for WASSA datasets (#1766)
    • Update BPE package (#1764)
    • Improve documentation (#1752 #1778)
    • Fix evaluation of TextClassifier if no label_type is passed (#1748)
    • Remove torch version checks that throw errors (#1744)
    • Update DaNE dataset URL (#1800)
    • Fix weight extraction error for empty sentences (#1805)
    Source code(tar.gz)
    Source code(zip)
  • v0.5.1(Jul 5, 2020)

    Release 0.5.1 with new features, datasets and models, including support for sentence transformers, transformer embeddings for arbitrary length sentences, new Dutch NER models, new tasks and more refactorings of evaluation and training routines to better organize the code!

    New Features and Enhancements:

    TransformerWordEmbeddings can now process long sentences (#1680)

    Adds a heuristic as a workaround to the max sequence length of some transformer embeddings, making it possible to now embed sequences of arbitrary length if you set allow_long_sentences=True, like so:

            allow_long_sentences=True, # set allow_long_sentences to True to enable this features

    Setting random seeds (#1671)

    It is now possible to set seeds when loading and downsampling corpora, so that the sample is always the same:

    # set a random seed 
    import random
    # load and downsample corpus
    corpus = SENTEVAL_MR(filter_if_longer_than=50).downsample(0.1)
    # print first sentence of dev and test 

    Make reprojection layer optional (#1676)

    Makes the reprojection layer optional in SequenceTagger. You can control this behavior through the reproject_embeddings parameter. If you set it to True, embeddings are reprojected via linear map to identical size. If set to False, no reprojection happens. If you set this parameter to an integer, the linear map maps embedding vectors to vectors of this size.

    # tagger with standard reprojection
    tagger = SequenceTagger(
    # tagger without reprojection
    tagger = SequenceTagger(
    # reprojection to vectors of length 128
    tagger = SequenceTagger(

    Set label name when predicting (#1671)

    You can now optionally specify the "label name" of the predicted label. This may be useful if you want to for instance run two different NER models on the same sentence:

    sentence = Sentence('I love Berlin')
    # load two NER taggers
    tagger_1 = SequenceTagger.load('ner')
    tagger_2 = SequenceTagger.load('ontonotes-ner')
    # specify label name of tagger_1 to be 'conll03_ner'
    tagger_1.predict(sentence, label_name='conll03_ner')
    # specify label name of tagger_2 to be 'onto_ner'
    tagger_1.predict(sentence, label_name='onto_ner')

    This may be useful if you have multiple ner taggers and wish to tag the same sentence with them. Then you can distinguish between the tags by the taggers. It is also now no longer possible to give the predict method a string - you now must pass a sentence.

    Sentence Transformers (#1696)

    Adds the SentenceTransformerDocumentEmbeddings class so you get embeddings from the sentence-transformer library. Use as follows:

    from flair.data import Sentence
    from flair.embeddings import SentenceTransformerDocumentEmbeddings
    # init embedding
    embedding = SentenceTransformerDocumentEmbeddings('bert-base-nli-mean-tokens')
    # create a sentence
    sentence = Sentence('The grass is green .')
    # embed the sentence

    You can find a full list of their pretained models here.

    Other enhancements

    • Update to transformers 3.0.0 (#1727)
    • Better Memory mode presets for classification corpora (#1701)
    • ClassificationDataset now also accepts line with "\t" seperator additionaly to blank spaces (#1654)
    • Change default fine-tuning in DocumentPoolEmbeddings to "none" (#1675)
    • Short-circuit the embedding loop (#1684)
    • Add option to pass kwargs into transformer models when initializing model (#1694)

    New Datasets and Models

    Two new dutch NER models (#1687)

    The new default model is a BERT-based RNN model with the highest accuracy:

    from flair.data import Sentence
    from flair.models import SequenceTagger
    # load the default BERT-based model
    tagger = SequenceTagger.load('nl-ner')
    # tag sentence
    sentence = Sentence('Ik hou van Amsterdam')

    You can also load a Flair-based RNN model (might be faster on some setups):

    # load the default BERT-based model
    tagger = SequenceTagger.load('nl-ner-rnn')

    Corpus of communicative functions (#1683) and pre-trained model (#1706)

    Adds corpus of communicate functions in scientific literature, described in this LREC paper and available here. Load with:


    We also ship a pre-trained model on this corpus, which you can load with:

    # load communicative function tagger
    tagger = TextClassifier.load('communicative-functions')
    # load communicative function tagger
    sentence = Sentence("However, previous approaches are limited in scalability .")
    # predict and print labels

    Keyword Extraction Corpora (#1629) and pre-trained model (#1689)

    Added 3 datasets available for keyphrase extraction via sequence labeling: Inspec, SemEval-2017 and Processed SemEval-2010

    Load like this:

    inspec_corpus = INSPEC()
    semeval_2010_corpus = SEMEVAL2010()
    semeval_2017 = SEMEVAL2017()

    We also ship a pre-trained model on this corpus, which you can load with:

    # load keyphrase tagger
    tagger = SequenceTagger.load('keyphrase')
    # load communicative function tagger
    sentence = Sentence("Here, we describe the engineering of a new class of ECHs through the "
                        "functionalization of non-conductive polymers with a conductive choline-based "
                        "bio-ionic liquid (Bio-IL).", use_tokenizer=True)
    # predict and print labels

    Swedish NER (#1652)

    Add corpus for swedish NER using dataset https://github.com/klintan/swedish-ner-corpus/. Load with:

    corpus = NER_SWEDISH()

    German Legal Named Entity Recognition (#1697)

    Adds corpus of legal named entities for German. Load with:

    corpus = LER_GERMAN()

    Refactoring of evaluation

    We made a number of refactorings to the evaluation routines in Flair. In short: whenever possible, we now use the evaluation methods of sklearn (instead of our own implementations which kept getting issues). This applies to text classification and (most) sequence tagging.

    A notable exception is "span-F1" which is used to evaluate NER because there is no good way of counting true negatives. After this PR, our implementation should now exactly mirror the original conlleval script of the CoNLL-02 challenge. In addition to using our reimplementation, an output file is now automatically generated that can be directly used with the conlleval script.

    In more detail, this PR makes the following changes:

    • Span is now a list of Token and can now be iterated like a sentence
    • flair.DataLoader is now used throughout
    • The evaluate() interface in the Model base class is changed so that it no longer requires a data loader, but ran run either over list of Sentence or a Dataset
    • SequenceTagger.evaluate() now explicitly distinguishes between F1 and Span-F1. In the latter case, no TN are counted (#1663) and a non-sklearn implementation is used.
    • In the evaluate() method of the SequenceTagger and TextClassifier, we now explicitly call the .predict()method.

    Bug fixes:

    • Fix figsize issue (#1622)
    • Allow strings to be passed instead of Path (#1637)
    • Fix segtok tokenization issue (#1653)
    • Serialize dropout in SequenceTagger (#1659)
    • Fix serialization error in DocumentPoolEmbeddings (#1671)
    • Fix subtokenization issues in transformers (#1674)
    • Add new datasets to init.py (#1677)
    • Fix deprecation warnings due to invalid escape sequences. (#1678)
    • Fix PooledFlairEmbeddings deserialization error (#1604)
    • Fix transformer tokenizer deserialization (#1686)
    • Fix issues caused by embedding mode and lambda functions in ELMoEmbeddings (#1692)
    • Fix serialization error in PooledFlairEmbeddings (#1593)
    • Fix mean pooling in PooledFlairEmbeddings (#1698)
    • Fix condition to assign whitespace_after attribute in the build_spacy_tokenizer wraper (#1700)
    • Fix WIKINER encoding for windows (#1713)
    • Detect and ignore empty sentences in BERT embeddings (#1716)
    • Fix error in returning multiple classes (#1717)
    Source code(tar.gz)
    Source code(zip)
  • v0.5(May 24, 2020)

    Release 0.5 with tons of new models, embeddings and datasets, support for fine-tuning transformers, greatly improved sentiment analysis models for English, tons of new features and big internal refactorings to better organize the code!

    New Fine-tuneable Transformers (#1494 #1544)

    Flair 0.5 adds support for transformers and fine-tuning with two new embeddings classes: TransformerWordEmbeddings and TransformerDocumentEmbeddings, for word- and document-level transformer embeddings respectively. Both classes can be initialized with a model name that indicates what type of transformer (BERT, XLNet, RoBERTa, etc.) you wish to use (check the full list Here)

    Transformer Word Embeddings

    If you want to embed the words in a sentence with transformers, do it like this:

    from flair.embeddings import TransformerWordEmbeddings
    # init embedding
    embedding = TransformerWordEmbeddings('bert-base-uncased')
    # create a sentence
    sentence = Sentence('The grass is green .')
    # embed words in sentence

    If instead you want to use RoBERTa, do:

    from flair.embeddings import TransformerWordEmbeddings
    # init embedding
    embedding = TransformerWordEmbeddings('roberta-base')
    # create a sentence
    sentence = Sentence('The grass is green .')
    # embed words in sentence

    Transformer Document Embeddings

    To get a single embedding for the whole document with BERT, do:

    from flair.embeddings import TransformerDocumentEmbeddings
    # init embedding
    embedding = TransformerDocumentEmbeddings('bert-base-uncased')
    # create a sentence
    sentence = Sentence('The grass is green .')
    # embed the sentence

    If instead you want to use RoBERTa, do:

    from flair.embeddings import TransformerDocumentEmbeddings
    # init embedding
    embedding = TransformerDocumentEmbeddings('roberta-base')
    # create a sentence
    sentence = Sentence('The grass is green .')
    # embed the sentence

    Text classification by fine-tuning a transformer

    Importantly, you can now fine-tune transformers to get state-of-the-art accuracies in text classification tasks. Use TransformerDocumentEmbeddings for this and set fine_tune=True. Then, use the following example code:

    from torch.optim.adam import Adam
    from flair.data import Corpus
    from flair.datasets import TREC_6
    from flair.embeddings import TransformerDocumentEmbeddings
    from flair.models import TextClassifier
    from flair.trainers import ModelTrainer
    # 1. get the corpus
    corpus: Corpus = TREC_6()
    # 2. create the label dictionary
    label_dict = corpus.make_label_dictionary()
    # 3. initialize transformer document embeddings (many models are available)
    document_embeddings = TransformerDocumentEmbeddings('distilbert-base-uncased', fine_tune=True)
    # 4. create the text classifier
    classifier = TextClassifier(document_embeddings, label_dictionary=label_dict)
    # 5. initialize the text classifier trainer with Adam optimizer
    trainer = ModelTrainer(classifier, corpus, optimizer=Adam)
    # 6. start the training
                  learning_rate=3e-5, # use very small learning rate
                  mini_batch_chunk_size=4, # optionally set this if transformer is too much for your machine
                  max_epochs=5, # terminate after 5 epochs

    New Taggers, Embeddings and Datasets

    Flair 0.5 adds a ton of new taggers, embeddings and datasets.

    New Taggers

    New sentiment models (#1613)

    We added new sentiment models for English. The new models are trained over a combined corpus of sentiment dataset, including Amazon product reviews. So they should be applicable to more domains than the old sentiment models that were only trained with movie reviews.

    There are two new models, a transformer-based model you can load like this:

    # load tagger
    classifier = TextClassifier.load('sentiment')
    # predict for example sentence
    sentence = Sentence("enormously entertaining for moviegoers of any age .")
    # check prediction

    And a faster, slightly less accurate model based on RNNs you can load like this:

    classifier = TextClassifier.load('sentiment-fast')

    Fine-grained POS models for English (#1625)

    Adds fine-grained POS models for English so you now have the option between 'pos' and 'upos' models for fine-grained and universal dependencies respectively. Load like this:

    # Fine-grained POS model
    tagger = SequenceTagger.load('pos')
    # Fine-grained POS model (fast variant)
    tagger = SequenceTagger.load('pos-fast')
    # Universal POS model
    tagger = SequenceTagger.load('upos')
    # Universal POS model (fast variant)
    tagger = SequenceTagger.load('upos-fast')

    Added Malayalam POS and XPOS tagger model (#1522)

    Added taggers for historical German speech and thought (#1532)

    New Embeddings

    Added language models for historical German by @redewiedergabe (#1507)

    Load the language models with:

    embeddings_forward = FlairEmbeddings('de-historic-rw-forward')
    embeddings_backward = FlairEmbeddings('de-historic-rw-backward')

    Added Malayalam flair embeddings models (#1458)

    embeddings_forward = FlairEmbeddings('ml-forward')
    embeddings_backward = FlairEmbeddings('ml-backward')

    Added Flair Embeddings from CLEF HIPE Shared Task (#1554)

    Adds the recently trained Flair embeddings on historic newspapers for German/English/French provided by the CLEF HIPE shared task.

    New Datasets

    Added NER dataset for Finnish (#1620)

    You can now load a Finnish NER corpus with

    ner_finnish = flair.datasets.NER_FINNISH()

    Added DaNE dataset (#1425)

    You can now load a Danish NER corpus with

    dane = flair.datasets.DANE()

    Added SentEval classification datasets (#1454)

    Adds 6 SentEval classification datasets to Flair:

    senteval_corpus_1 = flair.datasets.SENTEVAL_CR()
    senteval_corpus_2 = flair.datasets.SENTEVAL_MR()
    senteval_corpus_3 = flair.datasets.SENTEVAL_SUBJ()
    senteval_corpus_4 = flair.datasets.SENTEVAL_MPQA()
    senteval_corpus_5 = flair.datasets.SENTEVAL_SST_BINARY()
    senteval_corpus_6 = flair.datasets.SENTEVAL_SST_GRANULAR()

    Added Sentiment Datasets (#1545)

    Adds two new sentiment datasets to Flair, namely AMAZON_REVIEWS, a very large corpus of Amazon reviews with sentiment labels, and SENTIMENT_140, a corpus of tweets labeled with sentiment.

    amazon_reviews = flair.datasets.AMAZON_REVIEWS()
    sentiment_140 = flair.datasets.SENTIMENT_140()

    Added BIOfid dataset (#1589)

    biofid = flair.datasets.BIOFID()


    Any DataPoint can now be labeled (#1450)

    Refactored the DataPoint class and classes that inherit from it (Token, Sentence, Image, Span, etc.) so that all have the same methods for adding and accessing labels.

    • DataPoint base class now defined labeling methods (closes #1449)
    • Labels can no longer be passed to Sentence constructor, so instead of:
    sentence_1 = Sentence("this is great", labels=[Label("POSITIVE")])

    you should now do:

    sentence_1 = Sentence("this is great")
    sentence_1.add_label('sentiment', 'POSITIVE')


    sentence_1 = Sentence("this is great").add_label('sentiment', 'POSITIVE')

    Note that Sentence labels now have a label_type (in the example that's 'sentiment').

    • The Corpus method _get_class_to_count is renamed to _count_sentence_labels
    • The Corpus method _get_tag_to_count is renamed to _count_token_labels
    • Span is now a DataPoint (so it has an embedding and labels)

    Embeddings module was split into smaller submodules (#1588)

    Split the previously huge embeddings.py into several submodules organized in an embeddings/ folder. The submodules are:

    • token.py for all TokenEmbeddings classes
    • document.py for all DocumentEmbeddings classes
    • image.py for all ImageEmbeddings classes
    • legacy.py for embeddings that are now deprecated
    • base.py for remaining basic classes

    All embeddings are still exposed through the embeddings package, so the command to load them doesn't change, e.g.:

    from flair.embeddings import FlairEmbeddings
    embeddings = FlairEmbeddings('news-forward')

    so specifying the submodule is not needed.

    Datasets module was split into smaller submodules (#1510)

    Split the previously huge datasets.py into several submodules organized in a datasets/ folder. The submodules are:

    • sequence_labeling.py for all sequence labeling datasets
    • document_classification.py for all document classification datasets
    • treebanks.py for all dependency parsed corpora (UD treebanks)
    • text_text.py for all bi-text datasets (currently only parallel corpora)
    • text_image.py for all paired text-image datasets (currently only Feidegger)
    • base.py for remaining basic classes

    All datasets are still exposed through the datasets package, so it is still possible to load corpora with

    from flair.datasets import TREC_6

    without specifying the submodule.

    Other refactorings

    • Refactor datasets for code legibility (#1394)

    Small refactorings on flair.datasets for easier code legibility and fewer redundancies, removing about 100 lines of code: (1) Moved the default sampling logic from all corpora classes to the parent Corpus class. You can now instantiate a Corpus only with a train file which will trigger the sampling. (2) Moved the default logic for identifying train, dev and test files into a dedicated method to avoid duplicates in code.

    • Extend string output of Sentence (#1452)


    New Features

    Add option to specify document delimiter for language model training (#1541)

    You now have the option of specifying a document_delimiter when training a LanguageModel. Say, you have a corpus of textual lists and use "[SEP]" to mark boundaries between two lists, like this:

    - blue
    - green
    - red
    - Berlin
    - Munich

    Then you can now train a language model by setting the document_delimiter in the TextCorpus and LanguageModel objects. This will make sure only documents as a whole will get shuffled during training (i.e. the lists in the above example):

    # your document delimiter
    delimiter = '[SEP]'
    # set it when you load the corpus
    corpus = TextCorpus(
    # set it when you init the language model
    language_model = LanguageModel(
    # train your language model as always
    trainer = LanguageModelTrainer(language_model, corpus)

    Allow column delimiter to be set in ColumnCorpus (#1526)

    Added the possibility to set a different column delimite for ColumnCorpus, i.e.

    corpus = ColumnCorpus(
        column_format={0: 'text', 1: 'ner'},
        column_delimiter='\t', # set a different delimiter

    if you want to read a tab-separated column corpus.

    Improvements in classification corpus datasets (#1545)

    There are a number of improvements for the ClassificationCorpus and ClassificationDataset classes:

    • It is now possible to select from three memory modes ('full', 'partial' and 'disk'). Use full if the entire dataset and all objects fit into memory. Use 'partial' if it doesn't and use 'disk' if even 'partial' does not fit.
    • It is also now possible to provide "name maps" to rename labels in datasets. For instance, some sentiment analysis datasets use '0' and '1' as labels, while some others use 'POSITIVE' and 'NEGATIVE'. By providing name maps you can rename labels so they are consistent across datasets.
    • You can now choose which splits to downsample (for instance you might want to downsample 'train' and 'dev' but not 'test')
    • You can now specify the option "filter_if_longer_than", to filter all sentences that have more than the number of provided whitespaces. This is useful to limit corpus size as some sentiment analysis datasets are gigantic.

    Added different ways to combine ELMo layers (#1547)

    Improved default annealing scheme to anneal against score and loss (#1570)

    Add new scheduler that uses dev score as main metric to anneal against, but additionally uses dev loss in case two epochs have the same dev score.

    Added option for hidden state position in FlairEmbeddings (#1571)

    Adds the option to choose which hidden state to use in FlairEmbeddings: either the state at the end of each word, or the state at the whitespace after. Default is the state at the whitespace after.

    You can change the default like this:

    embeddings = FlairEmbeddings('news-forward', with_whitespace=False)

    This configuration seems to be better for syntactic tasks. For POS tagging, it seems that you should set with_whitespace=False. For instance, on UD_ENGLISH POS-tagging, we get 96.56 +- 0.03 with whitespace and 96.72 +- 0.04 without, averaged over three runs.

    See the discussion in #1362 for more details.

    Other features

    • Added the option of passing different tokenizers when loading classification datasets (#1579)

    • Added option for true whitespaces in ColumnCorpus #1583

    • Configurable cache_root from environment variable (#507)

    Performance improvements

    • Improve performance for loading not-in-memory corpus (#1413)

    • A new lmdb based alternative backend for word embeddings (#1515 #1536)

    • Slim down requirements (#1419)

    Bug Fixes

    • Fix issue where flair was crashing for cpu only version of pytorch (#1393 #1418)

    • Fix GPU memory error in PooledFlairEmbeddings (#1417)

    • Various small fixes (#1402 #1533 #1511 #1560 #1616)

    • Improve documentation (#1446 #1447 #1520 #1525 #1556)

    • Fix various issues in classification datasets (#1499)

    Source code(tar.gz)
    Source code(zip)
  • v0.4.5(Jan 24, 2020)

    This is an enhancement release that slims down Flair for quicker/easier installation and smaller library size. It also makes Flair compatible with torch 1.4.0 and adds enhancements that reduce model size and improve runtime speed for some embeddings. New features include the ability to steer the precision/recall tradeoff during training of models and support for CamemBERT embeddings.

    Memory, Runtime and Dependency Improvements

    Slim down dependency tree (#1296 #1299 #1335 #1336)

    We want to keep list of dependencies of Flair generally small to avoid errors like #1245 and keep the library small and quick to setup. So we removed dependencies that were each only used for one particular feature, namely:

    • ipython and ipython-genutils, only used for visualization settings in iPython notebooks
    • tiny_tokenizer, used for Japanese tokenization (replaced with instructions for how to install for all users who want to use Japanese tokenizers)
    • pymongo, used for MongoDB datasets (replaced with instructions for how to install for all users who want to use MongoDB datasets)
    • torchvision, now only loaded when needed

    We also relaxed version requirements for easier installation on Google CoLab (#1335 #1336)

    Dramatic speed-up of BERT embeddings (#1308)

    @shoarora optimized the BERTEmbeddings implementation by removing redundant calls. This was shown to lead to dramatic speed improvements.

    Reduce size of models that use WordEmbeddings (#1315)

    @timnon added a method to replace word embeddings in trained model with sqlite database to dramatically reduce memory usage. Creates class WordEmbeedingsStore which can be used to replace a WordEmbeddings-instance in a flair model via duck-typing. By using this, @timnon was able to reduce our ner-servers memory consumption from 6gig to 600mb (10x decrease) by adding a few lines of code. It can be tested using the following lines (also in the docstring). First create a headless version of a model without word embeddings:

    from flair.inference_utils import WordEmbeddingsStore
    from flair.models import SequenceTagger
    import pickle
    tagger = SequenceTagger.load("multi-ner-fast")
    pickle.dump(tagger, open("multi-ner-fast-headless.pickle", "wb"))

    and then to run the stored headless model without word embeddings, use:

    from flair.data import Sentence
    tagger = pickle.load(open("multi-ner-fast-headless.pickle", "rb"))
    text = "Schade um den Ameisenbären. Lukas Bärfuss veröffentlicht Erzählungen aus zwanzig Jahren."
    sentence = Sentence(text)

    New Features

    Prioritize precision/recall or specific classes during training (#1345)

    @klasocki added ways to steer the precision/recall tradeoff during training of models, as well as prioritize certain classes. This option was added to the SequenceTagger and the TextClassifier.

    You can steer precision/recall tradeoff by adding the beta parameter, which indicates how many more times recall is important than precision. So if you set beta=0.5, precision becomes twice as important than recall. If you set beta=2, recall becomes twice as important as precision. Do it like this:

    tagger = SequenceTagger(

    If you want to prioritize classes, you can pass a weight_loss dictionary to the model classes. For instance, to prioritize learning the NEGATIVE class in a sentiment tagger, do:

    tagger = TextClassifier(
        loss_weights={'NEGATIVE': 10.})

    which will increase the importance of class NEGATIVE by a factor of 10.

    CamemBERT Embeddings (#1297)

    @stefan-it added support for the recently proposed French language model: CamemBERT.

    Thanks to the awesome 🤗/Transformers library, CamemBERT can be used in Flair like in this example:

    from flair.data import Sentence
    from flair.embeddings import CamembertEmbeddings
    embedding = CamembertEmbeddings()
    sentence = Sentence("J'aime le camembert !")
    for token in sentence.tokens:

    Bug fixes and enhancements

    • Fix new RNN format for torch 1.4.0 (#1360, #1382 )
    • Fix memory issue in PooledFlairEmbeddings (#1337 #1339)
    • Correct subtoken mapping function for GPT-2 and RoBERTa (#1242)
    • Update the transformers library to the latest 2.3 version (#1333)
    • Add staticmethod decorator to some functions (#1257)
    • Add a warning if validation data is too small (#1115)
    • Remove leftover printline from MUSE embeddings (#1224)
    • Correct generate_text() UTF-8 conversion (#1238)
    • Clarify documentation (#1295 #1332)
    • Replace sklearn by scikit-learn (#1321)
    • Fix off-by-one error in progress logging (#1334)
    • Fix typo and annotation (#1341)
    • Various improvements (#1347)
    • Make load_big_file work with read-only file (#1353)
    • Rename tiny_tokenizer to konoha (#1363)
    • Make test loss plotting optional (#1372)
    • Add pretty print function for Dictionary (#1375)
    Source code(tar.gz)
    Source code(zip)
  • v0.4.4(Oct 20, 2019)

    Release 0.4.4 introduces dramatic improvements in inference speed for taggers (thanks to many contributions by @pommedeterresautee), Flair embeddings in 300 languages (thanks @stefan-it), modular tokenization and many new features and refactorings.

    Speed optimizations

    Many refactorings by @pommedeterresautee to improve inference speed of sequence tagger (#1038 #1053 #1068 #1093 #1130), Flair embeddings (#1074 #1095 #1107 #1132 #1145), word embeddings (#1084), embeddings memory management (#1082 #1117), general optimizations (#1112) and classification (#1187).

    The combined improvements increase inference speed by a factor of 2-3!

    New features

    Modular tokenization (#1022)

    You can now pass custom tokenizers to Sentence objects and Dataset loaders to use different tokenizers than the included segtok library by implementing a tokenizer method. Currently, in-built support exists for whitespace tokenization, segtok tokenization and Japanese tokenization with mecab (requires mecab to be installed). In the future, we expect support for additional external tokenizers to be added.

    For instance, if you wish to use Japanese tokanization performed by mecab, you can instantiate the Sentence object like this:

    from flair.data import build_japanese_tokenizer
    from flair.data import Sentence
    # instantiate Japanese tokenizer
    japanese_tokenizer = build_japanese_tokenizer()
    # init sentence and pass this tokenizer
    sentence = Sentence("私はベルリンが好きです。", use_tokenizer=japanese_tokenizer)

    Flair Embeddings for 300 languages (#1146)

    Thanks to @stefan-it, there is now a massivey multilingual Flair embeddings model that covers 300 languages. See #1099 for more info on these embeddings and this repo for more details.

    This replaces the old multilingual Flair embeddings that were trained for 6 languages. Load them with:

    embeddings_fw = FlairEmbeddings('multi-forward')
    embeddings_bw = FlairEmbeddings('multi-backward')

    Multilingual Character Dictionaries (#1157)

    Adds two multilingual character dictionaries computed by @stefan-it.

    Load with

    dictionary = Dictionary.load('chars-large')
    dictionary = Dictionary.load('chars-xl')

    Batch-growth annealing (#1138)

    The paper Don't Decay the Learning Rate, Increase the Batch Size makes the case for increasing the batch size over time instead of annealing the learning rate.

    This version adds the possibility to have arbitrarily large mini-batch sizes with an accumulating gradient strategy. It introduces the parameter mini_batch_chunk_size that you can set to break down large mini-batches into smaller chunks for processing purposes.

    So let's say you want to have a mini-batch size of 128, but your memory cannot handle more than 32 samples at a time. Then you can train like this:

    trainer = ModelTrainer(tagger, corpus)
        # set large mini-batch size
        # set chunk size to lower memory requirements

    Because we now can arbitrarly raise mini-batch size, we can now execute the annealing strategy in the above paper. Do it like this:

    trainer = ModelTrainer(tagger, corpus)
        # set initial mini-batch size
        # choose batch growth annealing 

    Document-level sequence labeling (#1194)

    Introduces the option for reading entire documents into one Sentence object for sequence labeling. This option is now supported for CONLL_03, CONLL_03_GERMAN and CONLL_03_DUTCH datasets which indicate document boundaries.

    Here's how to train a model on CoNLL-03 on the document level:

    # read CoNLL-03 with document_as_sequence=True
    corpus = CONLL_03(in_memory=True, document_as_sequence=True)
    # what tag do we want to predict?
    tag_type = 'ner'
    # 3. make the tag dictionary from the corpus
    tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
    # init simple tagger with GloVe embeddings
    tagger: SequenceTagger = SequenceTagger(
    # initialize trainer
    from flair.trainers import ModelTrainer
    trainer: ModelTrainer = ModelTrainer(tagger, corpus)
    # start training
        # set a much smaller mini-batch size because documents are huge

    Option to evaluate on training split (#1202)

    Previously, the ModelTrainer only allowed monitoring of dev and test splits during training. Now, you can also monitor the train split to better check if your method is overfitting.

    Support for Danish tagging (#1183)

    Adds support for Danish POS and NER thanks to @AmaliePauli!

    Use like this:

    from flair.data import Sentence
    from flair.models import SequenceTagger
    # example sentence
    sentence = Sentence("København er en fantastisk by .")
    # load Danish NER model and predict
    ner_tagger = SequenceTagger.load('da-ner')
    # print annotations (NER)
    # load Danish POS model and predict
    pos_tagger = SequenceTagger.load('da-pos')
    # print annotations (NER + POS)

    Support for DistilBERT embeddings (#1044)

    You can use them like this:

    from flair.data import Sentence
    from flair.embeddings import BertEmbeddings
    embeddings = BertEmbeddings("distilbert-base-uncased")
    s = Sentence("Berlin and Munich are nice cities .")
    for token in s.tokens:

    MongoDataset for reading text classification data from a Mongo database (#1192)

    Adds the option of reading data from MongoDB. See this documentation on how to use this features.

    Feidegger corpus (#1199)

    Adds a dataset downloader for the Feidegger corpus consisting of text-image pairs. Instantiate the corpus like this:

    from flair.datasets import FeideggerCorpus
    # instantiate Feidegger corpus
    corpus = FeideggerCorpus()
    # print a text-image pair


    Refactor checkpointing mechanism (#1101)

    Refactored the checkpointing mechanism and slimmed down interfaces / code required to load checkpoints.

    In detail:

    • The methods save_checkpoint and load_checkpoint are no longer part of the flair.nn.Model interface. Instead, saving and restoring checkpoints is now (fully) performed by the ModelTrainer.
    • The optimizer state and scheduler state are removed from the ModelTrainer constructor since they are no longer required here.
    • Loading a checkpoint is now one line of code (previously two lines).
    # 1. initialize trainer as always with a model and a corpus
    from flair.trainers import ModelTrainer
    trainer: ModelTrainer = ModelTrainer(model, corpus)
    # 2. train your model for 2 epochs
        # example checkpointing
    # 3. load last checkpoint with one line of code
    trainer = ModelTrainer.load_checkpoint('experiment/folder/checkpoint.pt', corpus)
    # 4. continue training for 2 extra epochs
    trainer.train('experiment/folder_2',  max_epochs=4) 

    Refactor data sampling during training (#1154)

    Adds a FlairSampler interface to better enable passing custom samplers to the ModelTrainer.

    For instance, if you want to always shuffle your dataset in chunks of 5 to 10 sentences, you provide a sampler like this:

    # your trainer
    trainer: ModelTrainer = ModelTrainer(tagger, corpus)
    # execute training run
                  # sample data in chunks of 5 to 10
                  sampler=ChunkSampler(block_size=5, plus_window=5)

    Other refactorings

    • Switch everything to batch first mode (#1077)

    • Refactor classification to be more consistent with SequenceTagger (#1151)

    • PyTorch-Transformers -> Transformers #1163

    • In-place transpose of tensors (#1047)


    Documentation fixes (#1045 #1098 #1121 #1157 #1160 #1168 )

    Add option to set rnn_type used in SequenceTagger (#1113)

    Accept string as input in NER predict (#1142)

    Example usage:

    # init tagger
    tagger= SequenceTagger.load('ner')
    # predict over list of strings
    sentences = tagger.predict(
            'George Washington went to Berlin .', 
            'George Berlin lived in Washington .'
    # output predictions
    for sentence in sentences:

    Enable One-hot Embeddings of other Tags (#1191)

    Bug fixes

    • Fix the learning rate finder (#1119)
    • Fix OneHotEmbeddings on Cuda (#1147)
    • Fix encoding error in CSVClassificationDataset (#1055)
    • Fix encoding errors related to old windows chars (#1135)
    • Fix length error in CharacterEmbeddings (#1088 )
    • Fix tokenizer insert empty token to sentence object (#1226)
    • Ensure StackedEmbeddings always has the same embedding order (#1114)
    • Use $HOME instead of ~ for cache_root (#1134)
    Source code(tar.gz)
    Source code(zip)
  • v0.4.3(Aug 26, 2019)

    Release 0.4.3 includes a host of new features including transformer-based embeddings (roBERTa, XLNet, XLM, etc.), fine-tuneable FlairEmbeddings, crosslingual MUSE embeddings, new data loading/sampling methods, speed/memory optimizations, bug fixes and enhancements. It also begins a refactoring of interfaces that prepares more general applicability of Flair to other types of downstream tasks.


    Transformer embeddings (#941 #972 #993)

    Updates the old pytorch-pretrained-BERT library to the latest version of pytorch-transformers to support various new Transformer-based architectures for embeddings.

    A total of 7 (new/updated) transformer-based embeddings can be used in Flair now:

    from flair.embeddings import (
    bert_embeddings = BertEmbeddings()
    gpt1_embeddings = OpenAIGPTEmbeddings()
    gpt2_embeddings = OpenAIGPT2Embeddings()
    txl_embeddings = TransformerXLEmbeddings()
    xlnet_embeddings = XLNetEmbeddings()
    xlm_embeddings = XLMEmbeddings()
    roberta_embeddings = RoBERTaEmbeddings()

    Detailed benchmarks on the downsampled CoNLL-2003 NER dataset for English can be found in #873 .

    Crosslingual MUSE Embeddings (#853)

    Use the new MuseCrosslingualEmbeddings class to embed any sentence in one of 30 languages into the same embedding space. Behind the scenes the class first does language detection of the sentence to be embedded, and then embeds it with the appropriate language embeddings. If you train a classifier or sequence labeler with (only) this class, it will automatically work across all 30 languages, though quality may widely vary.

    Here's how to embed:

    # initialize embeddings
    embeddings = MuseCrosslingualEmbeddings()
    # two sentences in different languages
    sentence_1 = Sentence("This red shoe is new .")
    sentence_2 = Sentence("Dieser rote Schuh ist rot .")
    # language code is auto-detected
    # embed sentences
    embeddings.embed([sentence_1, sentence_2])
    # print similarities
    cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)
    for token_1, token_2 in zip (sentence_1, sentence_2):
        print(f"'{token_1.text}' and '{token_2.text}' similarity: {cos(token_1.embedding, token_2.embedding)}")

    FastTextEmbeddings (#879 )

    Adds FastTextEmbeddings capable of handling for oov words. Be warned though that these embeddings are huge. BytePairEmbeddings are much smaller and reportedly of similar quality so it is probably advisable to use those instead.

    Fine-tuneable FlairEmbeddings (#922)

    You can now fine-tune FlairEmbeddings on downstream tasks. You can fine-tune an existing LM by simply passing the fine_tune parameter in the FlairEmbeddings constructor, like this:

    embeddings = FlairEmbeddings('news-foward', fine_tune=True)

    You can also use this option to task-train a wholly new language model by passing an empty LanguageModel to the FlairEmbeddings constructor and the fine_tune parameter, like this:

    # make an empty language model
    language_model = LanguageModel(
    # init FlairEmbeddings to task-train this model
    embeddings = FlairEmbeddings(language_model, fine_tune=True)


    Automatic mixed precision support (#934)

    Mixed precision training can significantly speed up training. It can now be enabled by setting use_amp=True in the trainer classes. For instance for training language models you can do:

    # train your language model
    trainer = LanguageModelTrainer(language_model, corpus)

    In our experiments, we saw 3x speedup of training large language models though results vary depending on your model size and experimental setup.

    Control memory / speed tradeoff during training (#891 #809).

    This release introduces the embeddings_storage_mode parameter to the ModelTrainer class and predict() methods. This parameter can be one of 'none', 'cpu' and 'gpu' and allows you to control the tradeoff between memory usage and speed during training:

    • If set to 'none' all embeddings are deleted after usage - this has lowest memory requirements but means that embeddings need to be recomputed at each epoch of training potentially causing a slowdown.
    • If set to 'cpu' all embeddings are moved to CPU memory after usage. During training, this means that they only need to be moved back to GPU for the forward pass, and not recomputed so in many cases this is faster, but requires memory.
    • If set to 'gpu' all embeddings stay on GPU memory after computation. This eliminates memory shuffling during training, causing a speedup. However this option requires enough GPU memory to be available for all embeddings of the dataset.

    To use this option during training, simply set the parameter:

            # initialize trainer
            trainer: ModelTrainer = ModelTrainer(tagger, corpus)

    This release also removes the FlairEmbeddings-specific disk-caching mechanism. In the future, a more general caching mechanism applicable to all embedding types may potentially be added as a fourth memory management option.

    Speed-ups on in-memory datasets (#792)

    A new DataLoader abstract base class used in Flair will speed up data loading for in-memory datasets.

    Refactoring of interfaces (#891 #843)

    This release also slims down interfaces of flair.nn.Model and adds a new DataPoint interface that is currently implemented by the Token and Sentence classes. The idea is to widen the applicability of Flair to other data types and other tasks. In the future, the DataPoint interface will for example also be implemented by an Image object and new downstream tasks added to Flair.

    The release also slims down the evaluate() method in the flair.nn.Model interface to take a DataLoader instead of a group of parameters. And refactors the logging header logic. Both refactorings prepare adding new new downstream tasks to Flair in the near future.

    Other features

    Training Classifiers with CSV files (#826 #952 #967)

    Adds the CSVClassificationCorpus so you can train classifiers directly from CSVs instead of first having to convert to FastText format. To load a CSV, you need to pass a column_name_map (like in ColumnCorpus), which indicates which column(s) in the CSV holds the text and which field(s) the label(s):

    corpus = CSVClassificationCorpus(
        # path to the data folder containing train / test / dev files
        # indicates which columns are text and labels
        column_name_map={4: "text", 1: "label_topic", 2: "label_subtopic"},
        # if CSV has a header, you can skip it

    Data sampling (#908)

    We added the first (of many) data samplers that can be passed to the ModelTrainer to influence training. The ImbalancedClassificationDatasetSampler for instance will upsample rare classes and downsample common classes in a classification dataset. It may potentially help with imbalanced datasets. Call like this:

        # initialize trainer
        trainer: ModelTrainer = ModelTrainer(tagger, corpus)

    There are two experimental chunk samplers (ChunkSampler and ExpandingChunkSampler) split a dataset into chunks and shuffle them. This preserves some ordering of the original data while also randomizing the data.


    • Adds HTML vizualization of sequence labeling (#933). Call like this:
    from flair.visual.ner_html import render_ner_html
    tagger = SequenceTagger.load('ner')
    sentence = Sentence(
        "Thibaut Pinot's challenge ended on Friday due to injury, and then Julian Alaphilippe saw "
        "his lead fall away. The BBC's Hugh Schofield in Paris reflects on 34 years of hurt."
    html = render_ner_html(sentence)
    with open("sentence.html", "w") as writer:
    • Plotter now returns images for use in iPython notebooks (#943)
    • Initial TensorBoard support (#924)
    • Add pointer to Flair Visualizer (#1014)

    Additional parameterization options

    • CharacterEmbeddings now let you specify number of hidden states and embedding size (#834)
    embedding = CharacterEmbedding(char_embedding_dim=64, hidden_size_char=64)
    • Adds configuration option for minimal learning rate stopping criterion (#871)
    • num_workers is a parameter of LanguageModelTrainer (#962 )

    Bug fixes / enhancements

    • Updates old pretrained models to remove old bugs / performance issues (#1017)
    • Fix error in RNN initialization in DocumentRNNEmbeddings (#793)
    • ELMoEmbeddings now use flair.device param (#825)
    • Fix download of TREC_6 dataset (#896)
    • Fix download of UD_GERMAN-HDT (#980)
    • Fix download of WikiNER_German (#1006)
    • Fix error in ColumnCorpus in which words that begin with hashtags were skipped as comments (#956)
    • Fix max_tokens_per_doc param in ClassificationCorpus (#991)
    • Simplify split rule in ColumnCorpus (#990)
    • Fix import error message for ELMoEmbeddings (#1019)
    • References to Persian language unified across embeddings (#773)
    • Updates most pre-trained models fixing quality issues / bugs (#800)
    • Clarifications in documentation (#803 #860 #868)
    • Fixes infinite loop for tokens without startpos (#1030)


    • Adds a learnable initial hidden state to SequenceTagger (#899)
    • Now keeps order of sentences in mini-batch when embedding (#866)
    • SequenceTagger now optionally returns a distribution of tag probabilities over all classes (#782 #949 #1016)
    • The model trainer now outputs a 'test.tsv' file that contains prediction of final model when done training (#771 )
    • Releases logging handler when finishing training a model (#799)
    • Fixes bad_epochs in training logs and no longer evaluates on test data at each epoch by default (#818 )
    • Convenience method to remove all empty sentences from a corpus (#795)
    Source code(tar.gz)
    Source code(zip)
  • v0.4.2(May 30, 2019)

    Release 0.4.2 includes new features such as streaming data loading (allowing training over very large datasets), support for OpenAI GPT Embeddings, pre-trained Flair embeddings for many new languages, better classification baselines using one-hot embeddings and fine-tuneable document pool embeddings, and text regression as a third task next to sequence labeling and text classification.

    New way of loading data (#768)

    The data loading part has been completely refactored to enable streaming data loading from disk using PyTorch's DataLoaders. I.e. training no longer requires the full dataset to be kept in memory, allowing us to train models over much larger datasets. This version also changes the syntax of how to load datasets.

    Old way (now deprecated):

    from flair.data_fetcher import NLPTaskDataFetcher, NLPTask
    corpus = NLPTaskDataFetcher.load_corpus(NLPTask.UD_ENGLISH)

    New way:

    import flair.datasets
    corpus = flair.datasets.UD_ENGLISH()

    To use streaming loading, i.e. to not load into memory, you can pass the in_memory parameter:

    import flair.datasets
    corpus = flair.datasets.UD_ENGLISH(in_memory=False)


    Flair embeddings (#614)

    This release brings Flair embeddings to 11 new languages (thanks @stefan-it!): Arabic (ar), Danish (da), Persian (fa), Finnish (fi), Hebrew (he), Hindi (hi), Croatian (hr), Indonesian (id), Italian (it), Norwegian (no) and Swedish (sv). It also improves support for Bulgarian (bg), Czech, Basque (eu), Dutch (nl) and Slovenian (sl), and adds special language models for historical German. Load with language code, i.e.

    # load Flair embeddings for Italian 
    embeddings = FlairEmbeddings('it-forward')

    One-hot encoded embeddings (#747)

    Some classification baselines work astonishingly well with simple learnable word embeddings. To support testing these baselines, we've added learnable word embeddings that start from a one-hot encoding of words. To initialize, you need to pass a corpus to initialize the vocabulary.

    # load corpus 
    import flair.datasets
    corpus = flair.datasets.UD_ENGLISH()
    # init learnable word embeddings with corpus
    embeddings = OneHotEmbeddings(corpus)

    More options in DocumentPoolEmbeddings (#747)

    We now allow users to specify a fine-tuning option before the pooling operation is executed in document pool embeddings. Options are 'none' (no fine-tuning), 'linear' (linear remapping of word embeddings), 'nonlinear' (nonlinear remapping of word embeddings). Nonlinear should be used together with WordEmbeddings, while None should be used with OneHotEmbeddings (not necessary since they are already learnt on data). So, to replicate FastText classification you can either do:

    # instantiate one-hot encoded word embeddings
    embeddings = OneHotEmbeddings(corpus)
    # document pool embeddings
    document_embeddings = DocumentPoolEmbeddings([embeddings], fine_tune_mode='none')


    # instantiate pre-trained word embeddings
    embeddings = WordEmbeddings('glove')
    # document pool embeddings
    document_embeddings = DocumentPoolEmbeddings([embeddings], fine_tune_mode='nonlinear')

    OpenAI GPT Embeddings (#624)

    We now support embeddings from the OpenAI GPT model. We use the excellent pytorch-pretrained-BERT library to download the GPT model, tokenize the input and extract embeddings from the subtokens.

    Initialize with:

    embeddings = OpenAIGPTEmbeddings()

    Portuguese embeddings from NILC (#576)

    Extensibility to new downstream tasks (#681)

    Previously, we had the SequenceTagger and TextClassifier as the two downstream tasks supported by Flair. The ModelTrainer had specific methods to train these two models, making it difficult for users to add new types of tasks (such as text regression) to Flair.

    This release refactors the flair.nn.Model and ModelTrainer functionality to make it uniform across tagging models and enable users to add new tasks to Flair. Now, by implementing the 5 methods in the flair.nn.Model interface, a custom model immediately becomes trainable with the ModelTrainer. Now, three types of downstream tasks implement this interface:

    • the SequenceTagger,
    • the TextClassifier
    • and the beta TextRegressor.

    The code refactor removes a lot of code redundancies and slims down the interfaces of the downstream tasks classes. As the sole breaking change, it removes the load_from_file() methods, which are now part of the load() method, i.e. if previously you loaded a self-trained model like this:

    tagger = SequenceTagger.load_from_file('/path/to/model.pt')

    You now do it like this:

    tagger = SequenceTagger.load('/path/to/model.pt')

    New features

    • New beta support for text regression (#564)
    • Return confidence scores for single-label classification (#664)
    • Add method to find probability for each class in case of multi-class classification (#693)
    • Capability to change threshold during multi label classification #707
    • Support for customized ELMo embeddings (#661)
    • Detect multi-label problems automatically: Previously, users always had to specify whether their text classification problem was multi_label or not. Now, this is detected automatically if users do not specify. So now you can init like this:
    # corpus
    corpus = TREC_6()
    # make label_dictionary
    label_dictionary = corpus.make_label_dictionary()
    # init text classifier
    classifier = TextClassifier(document_embeddings, label_dictionary)
    • We added better module descriptions to embeddings and dropout so that more parameters get printed by default for models for better logging. (#747)
    • Make 'cache_root' a global variable so that different directories can be chosen for caching (#667)
    • Both string and Token objects can now be passed to the add_token method (#628)

    New datasets

    • Added IMDB classification corpus to flair.datasets (#749)
    • Added TREC_6 classification corpus to flair.datasets (#749)
    • Added 20 newsgroups classification corpus to flair.datasets (NEWSGROUPS object)
    • WASSA-17 emotion intensity text regression tasks (#695)

    Bug fixes

    • We normalized the training loss across modules so that train / test loss are consistent. (#670)
    • Permission error on Windows preventing model download (#557)
    • Handling of empty sentences (#566 #758)
    • Fix text generation on CUDA (#666)
    • others ...
    Source code(tar.gz)
    Source code(zip)
  • v0.4.1(Feb 22, 2019)

    Release 0.4.1 with lots of new features, new embeddings (RNN, Transformer and BytePair embeddings), new languages (Japanese, Spanish, Basque), new datasets, bug fixes and speed improvements (2x training speed for language models).

    New Embeddings

    Biomedical Embeddings

    Added first embeddings trained over PubMed data, namely

    Load these for instance with:

    # Flair embeddings PubMed
    flair_embedding_forward = FlairEmbeddings('pubmed-forward')
    flair_embedding_backward = FlairEmbeddings('pubmed-backward')
    # ELMo embeddings PubMed
    elmo_embeddings = ELMoEmbeddings('pubmed')

    Byte Pair Embeddings

    Added the byte pair embeddings library by @bheinzerling. Support for 275 languages. Very useful if you want to train small models. Load these for instance with:

    # initialize embeddings
    embeddings =  BytePairEmbeddings(language='en')

    Transformer-XL Embeddings

    Transformer-XL embeddings added by @stefan-it. Load with:

    # initialize embeddings
    embeddings = TransformerXLEmbeddings()

    ELMo Transformer Embeddings

    Experimental transformer version of ELMo embeddings added by @stefan-it.


    The new DocumentRNNEmbeddings class replaces the now-deprecated DocumentLSTMEmbeddings. This class allows you to choose which type of RNN you want to use. By default, it uses a GRU.

    Initialize like this:

    from flair.embeddings import WordEmbeddings, DocumentRNNEmbeddings
    glove_embedding = WordEmbeddings('glove')
    document_lstm_embeddings = DocumentRNNEmbeddings([glove_embedding], rnn_type='LSTM')

    New languages


    FlairEmbeddings for Japanese trained by @frtacoa and @minh-agent:

    # forward and backward embedding
    embeddings_fw = FlairEmbeddings('japanese-forward')
    embeddings_bw = FlairEmbeddings('japanese-backward')


    Added pre-computed FlairEmbeddings for Spanish. Embeddings were computed over Wikipedia by @iamyihwa (see #80 )

    To load Spanish FlairEmbeddings, simply do:

    # default forward and backward embedding
    embeddings_fw = FlairEmbeddings('spanish-forward')
    embeddings_bw = FlairEmbeddings('spanish-backward')
    # CPU-friendly forward and backward embedding
    embeddings_fw_fast = FlairEmbeddings('spanish-forward-fast')
    embeddings_bw_fast = FlairEmbeddings('spanish-backward-fast')


    • @stefan-it trained FlairEmbeddings for Basque which we now include, load with:
    forward_lm_embeddings = FlairEmbeddings('basque-forward')
    backward_lm_embeddings = FlairEmbeddings('basque-backward')
    • add Basque FastText embeddings, load with:
    wikipedia_embeddings = WordEmbeddings('eu-wiki')
    crawl_embeddings = WordEmbeddings('eu-crawl')

    New Datasets

    • IMDB dataset #410 - load with
    corpus = NLPTaskDataFetcher.load_corpus(NLPTask.IMDB)
    • TREC_6 and TREC_50 #450 - load with
    corpus = NLPTaskDataFetcher.load_corpus(NLPTask.TREC_6)
    • adds download routines for Basque Universal Dependencies and Named Entities, load with
    corpus = NLPTaskDataFetcher.load_corpus(NLPTask.UD_BASQUE)
    corpus_ner = NLPTaskDataFetcher.load_corpus(NLPTask.NER_BASQUE)

    Other features

    FlairEmbeddings for long text

    FlairEmbeddings can now be generated for arbitrarily long strings without causing out of memory errors. See #444

    Function for calculating perplexity of a string #531

    Use like this:

    from flair.embeddings import FlairEmbeddings
    # get language model
    language_model = FlairEmbeddings('news-forward-fast').lm
    # calculate perplexity for grammatical sentence
    grammatical = 'The company made a profit'
    perplexity_gramamtical_sentence = language_model.calculate_perplexity(grammatical)
    # calculate perplexity for ungrammatical sentence
    ungrammatical = 'Nook negh qapla!'
    perplexity_ungramamtical_sentence = language_model.calculate_perplexity(ungrammatical)
    # print both
    print(f'"{grammatical}" - perplexity is {perplexity_gramamtical_sentence}')
    print(f'"{ungrammatical}" - perplexity is {perplexity_ungramamtical_sentence}')

    Bug fixes

    • Overflow error in text generation #322
    • Sentence embeddings are now vectors #368
    • macro average F-score computation #521
    • character embeddings on CUDA #434
    • accuracy calculation #553

    Speed improvements

    • Asynchronous loading of mini batches in language model training (roughly doubles training speed) #406
    • Only send mini-batches to GPU #350
    • Speed up sequence tagger prediction #353
    • Use new cuda semantics #402
    • Reduce CPU-GPU shuffling #459
    • LM memory tweaks #466
    Source code(tar.gz)
    Source code(zip)
  • v0.4.0(Dec 19, 2018)

    Release 0.4 with new models, lots of new languages, experimental multilingual models, hyperparameter selection methods, BERT and ELMo embeddings, etc.

    New Features

    Support for new languages

    Flair embeddings

    We now include new language models for:

    In addition to English and German. You can load FlairEmbeddings for Dutch for instance with:

    flair_embeddings = FlairEmbeddings('dutch-forward')

    Word Embeddings

    We now include pre-trained FastText Embeddings for 30 languages: English, German, Dutch, Italian, French, Spanish, Swedish, Danish, Norwegian, Czech, Polish, Finnish, Bulgarian, Portuguese, Slovenian, Slovakian, Romanian, Serbian, Croatian, Catalan, Russian, Hindi, Arabic, Chinese, Japanese, Korean, Hebrew, Turkish, Persian, Indonesian.

    Each language has embeddings trained over Wikipedia, or Web crawls. So instantiate with:

    # German embeddings computed over Wikipedia
    german_wikipedia_embeddings = WordEmbeddings('de-wiki')
    # German embeddings computed over web crawls
    german_crawl_embeddings = WordEmbeddings('de-crawl')

    Named Entity Recognition

    Thanks to the Flair community, we now include NER models for:

    Next to the previous models for English and German.

    Part-of-Speech Taggigng

    Thanks to the Flair community, we now include PoS models for:

    Multilingual models

    As a major new feature, we now include models that can tag text in various languages.

    12-language Part-of-Speech Tagging

    We include a PoS model trained over 12 different languages (English, German, Dutch, Italian, French, Spanish, Portuguese, Swedish, Norwegian, Danish, Finnish, Polish, Czech).

    # load model
    tagger = SequenceTagger.load('pos-multi')
    # text with English and German sentences
    sentence = Sentence('George Washington went to Washington . Dort kaufte er einen Hut .')
    # predict PoS tags
    # print sentence with predicted tags

    4-language Named Entity Recognition

    We include a NER model trained over 4 different languages (English, German, Dutch, Spanish).

    # load model
    tagger = SequenceTagger.load('ner-multi')
    # text with English and German sentences
    sentence = Sentence('George Washington went to Washington . Dort traf er Thomas Jefferson .')
    # predict NER tags
    # print sentence with predicted tags

    This model also kind of works on other languages, such as French.

    Pre-trained classification models (issue 70)

    Flair now also includes two pre-trained classification models:

    • de-offensive-lanuage: detecting offensive language in German text (GermEval 2018 Task 1)
    • en-sentiment: detecting postive and negative sentiment in English text (IMDB)

    Simply load the TextClassifier using the preferred model, such as


    BERT and ELMo embeddings

    We added both BERT and ELMo embeddings so you can try them out, and mix and match them with Flair embeddings or any other embedding types. We hope this will enable the research community to better compare and combine approaches.

    BERT Embeddings (issue 251)

    We added BERT embeddings to Flair. We are using the implementation of huggingface. The embeddings can be used as any other embedding type in Flair:

    from flair.embeddings import BertEmbeddings
     # init embedding
    embedding = BertEmbeddings()
     # create a sentence
    sentence = Sentence('The grass is green .')
     # embed words in sentence

    ELMo Embeddings (issue 260)

    Flair now also includes ELMo embeddings. We use the implementation of AllenNLP. As this implementation comes with a lot of sub-dependencies, you need to first install the library via pip install allennlp before you can use it in Flair. Using the embeddings is as simple as using any other embedding type:

    from flair.embeddings import ELMoEmbeddings
    # init embedding
    embedding = ELMoEmbeddings()
    # create a sentence
    sentence = Sentence('The grass is green .')
    # embed words in sentence

    Multi-Dataset Training (issue 232)

    You can now train a model on on multiple datasets with the MultiCorpus object. We use this to train our multilingual models.

    Just create multiple corpora and put them into MultiCorpus:

    english_corpus = NLPTaskDataFetcher.load_corpus(NLPTask.UD_ENGLISH)
    german_corpus = NLPTaskDataFetcher.load_corpus(NLPTask.UD_GERMAN)
    dutch_corpus = NLPTaskDataFetcher.load_corpus(NLPTask.UD_DUTCH)
    multi_corpus = MultiCorpus([english_corpus, german_corpus, dutch_corpus])

    The multi_corpus can now be used for training, just as any other corpus before. Check the tutorial for more details.

    Parameter Selection using Hyperopt (issue 242)

    We built a wrapper around hyperopt to allow you to search for the best hyperparameters for your downstream task.

    Define your search space and start training using several different parameter settings. The results are written to a specific file called param_selection.txt in the result directory. Check the tutorial for more details.

    NLP Dataset Downloader (issue 243)

    To make it as easy as possible to start training models, we have a new feature for automatically downloading publicly available NLP datasets. For instance, by running this code:

    corpus = NLPTaskDataFetcher.load_corpus(NLPTask.UD_ENGLISH)

    you download the Universal Dependencies corpus for English and can immediately start training models. The list of available datasets can be found in the tutorial.

    Model training features

    We added various other features to model training.

    Saving training log (issue 212)

    The training log output will from now on be automatically saved in the result directory you provide for training. The log will be saved in training.log.

    Resuming training (issue 217)

    It is now possible to stop training at any point in time and to resume it later by training with checkpoint set to True. Check the tutorial for more details.

    Custom Optimizers (issue 220)

    You can now choose other optimizers besides SGD, i.e. any PyTorch optimizer, plus our own modified implementations of SDG and Adam, namely SGDW and AdamW.

    Learning Rate Finder (issue 228)

    A new helper method to assist you in finding a good learning rate for model training.

    Breaking Changes

    This release introduces breaking changes. The most important are:

    Unified Model Trainer (issue 189)

    Instead of maintaining two separate trainer classes for sequence labeling and text classification, we now have one model training class, namely ModelTrainer. This replaces the earlier classes SequenceTaggerTrainer and TextClassifierTrainer.

    Downstream task models now implement the new flair.nn.Model interface. So, both the SequenceTagger and TextClassifier now inherit from flair.nn.Model. This allows both models to be trained with the ModelTrainer, like this:

    # Training text classifier
    tagger = SequenceTagger(512, embeddings, tag_dictionary, 'ner')
    trainer = ModelTrainer(tagger, corpus)
    # Training text classifier
    classifier = TextClassifier(document_embedding, label_dictionary=label_dict)
    trainer = ModelTrainer(classifier, corpus)

    The advantage is that all training parameters ans training procedures are now the same for sequence labeling and text classification, which reduces redundancy and hopefully make it easier to understand.

    Metric class

    The metric class is now refactored to compute micro and macro averages for F1 and accuracy. There is also a new enum EvaluationMetric which you can pass to the ModelTrainer to tell it what to use for evaluation.

    Updates and Bug Fixes

    Torch 1.0 (issue 176)

    Flair now bulids on torch 1.0.

    Use Pathlib (issue 176)

    Flair now uses Path wherever possible to allow easier operations on files/directories. However, our interfaces still allows you to pass a string, which will then be transformed into a Path by Flair.

    Bug Fixes

    • Fix: Non-whitespaced tokenized text results into an infinite loop (issue 226)
    • Fix: Getting IndexError: list index out of range error (issue 233)
    • Do not reset cache directory always to None (issue 249)
    • Filter sentences with zero tokens (issue 266)
    Source code(tar.gz)
    Source code(zip)
  • v0.3.2(Nov 12, 2018)

    This is an update over release 0.3.1 with some critical bug fixes, a few new features and a lot more pre-packaged embeddings.

    New Features


    More word embeddings (#194 )

    We added FastText embeddings for 10 languages ('en', 'de', 'fr', 'pl', 'it', 'es', 'pt', 'nl', 'ar', 'sv'), load using the two-letter language code, like this:

    french_embedding = WordEmbeddings('fr')

    More character LM embeddings (#204 #187 )

    Thanks to contribution by @stefan-it, we added CharLMEmbeddings for Bulgarian and Slovenian. Load like this:

    flm_embeddings = CharLMEmbeddings('slovenian-forward')
    blm_embeddings = CharLMEmbeddings('slovenian-backward')

    Custom embeddings (#170 )

    Add explanation on how to use your own custom word embeddings. Simply convert to gensim.KeyedVectors and point embedding class there:

    custom_embedding = WordEmbeddings('path/to/your/custom/embeddings.gensim')

    New embeddings type: DocumentPoolEmbeddings (#191 )

    Add a new embedding class for document-level embeddings. You can now choose between different pooling options, e.g. min, max and average. Create the new embeddings like this:

    word_embeddings = WordEmbeddings('glove')
    pool_embeddings = DocumentPoolEmbeddings([word_embeddings], mode='min')

    Language model

    New method: generate_text() (#167 )

    The LanguageModel class now has an in-built generate_text() method to sample the LM. Run code like this:

    # load your language model
    model = LanguageModel.load_language_model('path/to/your/lm')
    # generate 2000 characters
    text = model.generate_text(20000)


    Class-based metrics in Metric class (#164 )

    Refactored Metric class to provide class-based metrics, as well as micro and macro averaged F1 scores.

    Bug Fixes

    Fix serialization error for MacOS and Windows (#174 )

    On these setups, we got errors when serializing or loading large models. We've put in place a workaround that limits model size so it works on those systems. Added bonus is that models are smaller now.

    "Frozen" dropout (#184 )

    Potentially big issue in which dropout was frozen in the first epoch in embeddings produced from the character LM, meaning that throughout training the same dimensions stayed dropped. Fixed this.

    Testing step in language model trainer (#178 )

    Previously, the language model was never applied to test data during training. A final testing step has been added in (again).


    Distinguish between unit and integration tests (#183)

    Instructions on how to run tests with pipenv (#161 )


    Disable autograd during testing and prediction (#175)

    Since autograd is unused here this gives us minor speedups.

    Source code(tar.gz)
    Source code(zip)
  • v0.3.1(Oct 19, 2018)

    This is a stability-update over release 0.3.0 with small optimizations, refactorings and bug fixes. For list of new features, refer to 0.3.0.


    Retain Token embeddings in memory by default (#146 )

    Allow for faster training of text classifier on large datasets by keeping token embeddings im memory.

    Always clear embeddings after prediction (#149 )

    After prediction, remove embeddings from memory to avoid filling up memory.


    Alignd TextClassificationTrainer and SquenceTaggerTrainer (#148 )

    Align signatures and features of the two training classes to make it easier to understand training options.

    Updated DocumentLSTMEmbeddings (#150 )

    Remove unused flag and code from DocumentLSTMEmbeddings

    Removed unneeded AWS and Jinja2 dependencies (#158 )

    Some dependencies are no longer required.

    Bug Fixes

    Fixed error when predicting over empty sentences. (#157)

    Serialization: reset cache settings when saving a model. (#153 )

    Source code(tar.gz)
    Source code(zip)
  • v0.3.0(Oct 16, 2018)

    Breaking Changes

    New Label class with confidence score (https://github.com/zalandoresearch/flair/issues/38)

    A tag prediction is not a simple string anymore but a Label, which holds a value and a confidence score. To obtain the tag name you need to call tag.value. To get the score call tag.score. This can help you build applications in which you only want to use predictions that lie above a specific confidence threshold.

    LockedDropout moved to the new flair.nn module (https://github.com/zalandoresearch/flair/issues/48)

    New Features

    Multi-token spans (https://github.com/zalandoresearch/flair/issues/54, https://github.com/zalandoresearch/flair/issues/97)

    Entities are can now be wrapped into multi-token spans (type: Span). This is helpful for entities that span multiple words, such as "George Washington". A Span contains the position of the entity in the original text, the tag, a confidence score, and its text. You can get spans from a sentence by using the get_spans() method, like so:

    from flair.data import Sentence
    from flair.models import SequenceTagger
    # make a sentence
    sentence = Sentence('George Washington went to Washington .')
    # load and run NER
    tagger = SequenceTagger.load('ner')
    # get span entities, together with tag and confidence score
    for entity in sentence.get_spans('ner'):
        print('{} {} {}'.format(entity.text, entity.tag, entity.score))

    Predictions with confidence score (https://github.com/zalandoresearch/flair/issues/38)

    Predicted tags are no longer simple strings, but objects of type Label that contain a value and a confidence score. These scores are extracted during prediction from the sequence tagger or text classifier and indicate how confident the model is of a prediction. Print confidence scores of tags like this:

    from flair.data import Sentence
    from flair.models import SequenceTagger
    # make a sentence
    sentence = Sentence('George Washington went to Washington .')
    # load the POS tagger
    tagger = SequenceTagger.load('pos')
    # run POS over sentence
    # print token, predicted POS tag and confidence score
    for token in sentence:
        print('{} {} {}'.format(token.text, token.get_tag('pos').value, token.get_tag('pos').score))

    Visualization routines (https://github.com/zalandoresearch/flair/issues/61)

    flair now includes visualizations for plotting training curves and weights when training a sequence tagger or text classifier. We also added visualization routines for plotting embeddings and highlighting tags in a sentence. For instance, to visualize contextual string embeddings, do this:

    from flair.data_fetcher import NLPTaskDataFetcher, NLPTask
    from flair.embeddings import CharLMEmbeddings
    from flair.visual import Visualizer
    # get a list of Sentence objects
    corpus = NLPTaskDataFetcher.fetch_data(NLPTask.CONLL_03).downsample(0.1)
    sentences = corpus.train + corpus.test + corpus.dev
    # init embeddings (can also be a StackedEmbedding)
    embeddings = CharLMEmbeddings('news-forward-fast')
    # embed corpus batch-wise
    batches = [sentences[x:x + 8] for x in range(0, len(sentences), 8)]
    for batch in batches:
    # visualize
    visualizer = Visualizer()
    visualizer.visualize_word_emeddings(embeddings, sentences, 'data/visual/embeddings.html')

    Implementation of different dropouts (https://github.com/zalandoresearch/flair/issues/48)

    Different dropout possibilities (Locked Dropout and Word Dropout) were added and can be used during training.

    Memory management for training on large data sets (https://github.com/zalandoresearch/flair/issues/137)

    flair now stores contextual string embeddings on disk to speed up training and allow for training on larger datsets.

    Pre-trained language models for Polish

    Added pre-trained language models for Polish, donated by (Borchmann et al., 2018). Load the Polish embeddings like this:

    flm_embeddings = CharLMEmbeddings('polish-forward')
    blm_embeddings = CharLMEmbeddings('polish-backward')

    Bug Fixes

    Fix evaluation of sequence tagger (https://github.com/zalandoresearch/flair/issues/79, https://github.com/zalandoresearch/flair/issues/75)

    The script eval.pl for sequence tagger contained bugs. flair now uses its own evaluation methods.

    Fix bugs in text classifier (https://github.com/zalandoresearch/flair/issues/108)

    Fixed bugs in single label training and out-of-memory errors during evaluation.


    Standardize logging output (https://github.com/zalandoresearch/flair/issues/16)

    Logging output for sequence tagger and text classifier is imporved and standardized.

    Update torch version (https://github.com/zalandoresearch/flair/issues/34, https://github.com/zalandoresearch/flair/issues/106)

    flair now uses torch version 0.4.1

    Updated documentation (https://github.com/zalandoresearch/flair/issues/138, https://github.com/zalandoresearch/flair/issues/89)

    Expanded documentation and tutorials.

    Source code(tar.gz)
    Source code(zip)
  • v0.2.0(Aug 3, 2018)

    Breaking Changes

    Reorganized package structure #12

    There are now two packages: flair.models and flair.trainers for the models and model trainers respectively.

    Models package

    flair.models contains 3 model classes: SequenceTagger, TextClassifier and LanguageModel.

    Trainers package

    flair.trainers contains 3 model trainer classes: SequenceTaggerTrainer, TextClassifierTrainer and LanguageModelTrainer.

    Direct import from package

    You call these classes directly from the packages, for instance the SequenceTagger is now instantiated as:

    from flair.models import SequenceTagger
    tagger = SequenceTagger.load('ner')

    Reorganized embeddings #12

    Clear distinction between token-level and document-level embeddings by adding two classes, namely TokenEmbeddings and DocumentEmbeddings from which respective embeddings need to inherit.

    New Features

    LanguageModelTrainer #24 #17

    Added LanguageModelTrainer class to train your own LM embeddings.

    Document Classification #10

    Added experimental TextClassifier model for document-level text classification. Also added corresponding model trainer class, i.e. TextClassifierTrainer.

    Batch prediction #7

    Added batching into prediction method for faster sequence tagging

    CPU-friendly pre-trained models #29

    Added pre-trained models with smaller LM embeddings for faster CPU-inference speed

    You can load them by adding '-fast' to the model name. Only for English at present.

    from flair.models import SequenceTagger
    tagger = SequenceTagger.load('ner-fast')

    Learning Rate Scheduling #19

    Added learning rate schedulers to all trainer classes for improved learning rate annealing functionality and control.

    Auto-spawn on GPUs #19

    All model classes now automatically spawn on GPUs if available. The separate .cuda() call is no longer necessary.

    Bug Fixes

    Retagging error #23

    Fixed error that occurred when using multiple pre-trained taggers on the same sentence.

    Empty sentence error #33

    Fixed error that caused data fetchers to sometimes create empty sentences.


    Unit Tests #15

    Added a large set of automated unit tests for better stability.

    Documentation #15

    Expanded documentation and tutorials. Also expanded descriptions of APIs.

    Code Simplifications in sequence tagger #19

    A number of code simplifications all around, hopefully making the code easier to understand.

    Source code(tar.gz)
    Source code(zip)
  • v0.1.0(Jul 13, 2018)

    First release of Flair Framework

    Static word embeddings:

    • includes prepared word embeddings from GloVe, FastText, Numberbatch and Extvec
    • includes prepared word embeddings for English, German and Swedish

    Contextual string embeddings:

    • includes pre-trained models for English and German

    Text embeddings:

    • Two experimental methods for full-text embeddings (LSTM and Mean)

    Sequence labeling:

    • pre-trained models for English (PoS-tagging, chunking and NER)
    • pre-trained models for German (PoS-tagging and NER)
    • experimental semantic frame detector for English
    Source code(tar.gz)
    Source code(zip)
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