Multilingual Emotion classification using BERT (fine-tuning). Published at the WASSA workshop (ACL2022).

Overview

XLM-EMO: Multilingual Emotion Prediction in Social Media Text

Abstract

Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the available emotion detection datasets across 19 languages. We train a multilingual emotion prediction model for social media data, XLM-EMO. The model shows competitive performance in a zero-shot setting, suggesting it is helpful in the context of low-resource languages. We release our model to the community so that interested researchers can directly use it.

See the paper for additional details:

Bianchi, F., Nozza, & D., Hovy. "XLM-EMO: Multilingual Emotion Prediction in Social Media Text". In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (Forthcoming). Association for Computational Linguistics, 2022. Link.


  • Free software: MIT license

Installing

pip install -U xlm-emo

Important: If you want to use CUDA you need to install the correct version of the CUDA systems that matches your distribution, see PyTorch.

Features

from xlm_emo.classifier import  EmotionClassifier
ec = EmotionClassifier()

ec.predict(["senti testa di cazzo", "I am very happy"])

>> ["anger", "joy"]

Models

Model Link Macro F1 on Test Set
XLM-EMO-T https://huggingface.co/MilaNLProc/xlm-emo-t 0.85
XLM-EMO-B TBD TBD
XLM-EMO-L TBD TBD

Reference

If you use this tool please cite the following paper:

@inproceedings{bianchi-etal-2022-xlmemo,
title = {{XLM-EMO}: Multilingual Emotion Prediction in Social Media Text},
author = "Bianchi, Federico and Nozza, Debora and Hovy, Dirk",
booktitle = "Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
year = "2022",
publisher = "Association for Computational Linguistics"
}

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

You might also like...
Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.

Kashgari Overview | Performance | Installation | Documentation | Contributing 🎉 🎉 🎉 We released the 2.0.0 version with TF2 Support. 🎉 🎉 🎉 If you

Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.

Kashgari Overview | Performance | Installation | Documentation | Contributing 🎉 🎉 🎉 We released the 2.0.0 version with TF2 Support. 🎉 🎉 🎉 If you

IMDB film review sentiment classification based on BERT's supervised learning model.
IMDB film review sentiment classification based on BERT's supervised learning model.

IMDB film review sentiment classification based on BERT's supervised learning model. On the other hand, the model can be extended to other natural language multi-classification tasks.

Text Classification in Turkish Texts with Bert
Text Classification in Turkish Texts with Bert

You can watch the details of the project on my youtube channel Project Interface Project Second Interface Goal= Correctly guessing the classification

A simple Speech Emotion Recognition (SER) API created using Flask and running in a Docker container.

emovoz Introduction A simple Speech Emotion Recognition (SER) API created using Flask and running in a Docker container. The SER system was built with

REST API for sentence tokenization and embedding using Multilingual Universal Sentence Encoder.

What is MUSE? MUSE stands for Multilingual Universal Sentence Encoder - multilingual extension (16 languages) of Universal Sentence Encoder (USE). MUS

💛 Code and Dataset for our EMNLP 2021 paper:
💛 Code and Dataset for our EMNLP 2021 paper: "Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes"

Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes Official PyTorch implementation and EmoCause evaluatio

A flask application to predict the speech emotion of any .wav file.

This is a speech emotion recognition app. It will allow you to train a modular MLP model with the RAVDESS dataset, and then use that model with a flask application to predict the speech emotion of any .wav file.

This code is the implementation of Text Emotion Recognition (TER) with linguistic features

APSIPA-TER This code is the implementation of Text Emotion Recognition (TER) with linguistic features. The network model is BERT with a pretrained mod

Owner
MilaNLP
MilaNLP
天池中药说明书实体识别挑战冠军方案;中文命名实体识别;NER; BERT-CRF & BERT-SPAN & BERT-MRC;Pytorch

天池中药说明书实体识别挑战冠军方案;中文命名实体识别;NER; BERT-CRF & BERT-SPAN & BERT-MRC;Pytorch

zxx飞翔的鱼 751 Dec 30, 2022
null 189 Jan 2, 2023
Framework for fine-tuning pretrained transformers for Named-Entity Recognition (NER) tasks

NERDA Not only is NERDA a mesmerizing muppet-like character. NERDA is also a python package, that offers a slick easy-to-use interface for fine-tuning

Ekstra Bladet 141 Dec 30, 2022
Applying "Load What You Need: Smaller Versions of Multilingual BERT" to LaBSE

smaller-LaBSE LaBSE(Language-agnostic BERT Sentence Embedding) is a very good method to get sentence embeddings across languages. But it is hard to fi

Jeong Ukjae 13 Sep 2, 2022
multi-label,classifier,text classification,多标签文本分类,文本分类,BERT,ALBERT,multi-label-classification,seq2seq,attention,beam search

multi-label,classifier,text classification,多标签文本分类,文本分类,BERT,ALBERT,multi-label-classification,seq2seq,attention,beam search

hellonlp 30 Dec 12, 2022
LV-BERT: Exploiting Layer Variety for BERT (Findings of ACL 2021)

LV-BERT Introduction In this repo, we introduce LV-BERT by exploiting layer variety for BERT. For detailed description and experimental results, pleas

Weihao Yu 14 Aug 24, 2022
VD-BERT: A Unified Vision and Dialog Transformer with BERT

VD-BERT: A Unified Vision and Dialog Transformer with BERT PyTorch Code for the following paper at EMNLP2020: Title: VD-BERT: A Unified Vision and Dia

Salesforce 44 Nov 1, 2022
Pytorch-version BERT-flow: One can apply BERT-flow to any PLM within Pytorch framework.

Pytorch-version BERT-flow: One can apply BERT-flow to any PLM within Pytorch framework.

Ubiquitous Knowledge Processing Lab 59 Dec 1, 2022
Code and datasets for our paper "PTR: Prompt Tuning with Rules for Text Classification"

PTR Code and datasets for our paper "PTR: Prompt Tuning with Rules for Text Classification" If you use the code, please cite the following paper: @art

THUNLP 118 Dec 30, 2022
Using Bert as the backbone model for lime, designed for NLP task explanation (sentence pair text classification task)

Lime Comparing deep contextualized model for sentences highlighting task. In addition, take the classic explanation model "LIME" with bert-base model

JHJu 2 Jan 18, 2022