Text Classification in Turkish Texts with Bert

Last update: Dec 31, 2022

Related tags

Text Data & NLP python deep-learning text-classification keras transformers embeddings audio-classification bert keras-tensorflow tensorflow2

Overview

You can watch the details of the project on my youtube channel

Project Interface

Project Second Interface

Goal= Correctly guessing the classification of texts and audios

BERT_Text_Classification

It is a text classification task implementation transformers (by HuggingFace) with BERT. It contains several parts:

--Data pre-processing

--BERT tokenization and input formating

--Train with BERT

--Evaluation

--Save and load saved model

Text-classification-transformers

Text classification tasks are most easily encountered in the area of natural language processing and can be used in various ways.

However, the given data needs to be preprocessed and the model's data pipeline must be created according to the preprocessing.

The purpose of this Repository is to allow text classification to be easily performed with Transformers (BERT)-like models if text classification data has been preprocessed into a specific structure.

Implemented based on Huggingfcae transformers for quick and convenient implementation.

Text Classification in Turkish Texts with Bert

Related tags

Overview

You can watch the details of the project on my youtube channel

Project Interface

Project Second Interface

BERT_Text_Classification

Text-classification-transformers

📝 read_dataset

Unique Categories

☄️ Available models

🏴‍☠️ Model Performance

Predictions Vs Actuals

🃏 predictor

97.22 📈

You might also like...

Using Bert as the backbone model for lime, designed for NLP task explanation (sentence pair text classification task)

Python package to easily retrain OpenAI's GPT-2 text-generating model on new texts

Python package to easily retrain OpenAI's GPT-2 text-generating model on new texts

Neural text generators like the GPT models promise a general-purpose means of manipulating texts.

LV-BERT: Exploiting Layer Variety for BERT (Findings of ACL 2021)

VD-BERT: A Unified Vision and Dialog Transformer with BERT

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

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

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

Owner

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

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

Ptorch NLU, a Chinese text classification and sequence annotation toolkit, supports multi class and multi label classification tasks of Chinese long text and short text, and supports sequence annotation tasks such as Chinese named entity recognition, part of speech tagging and word segmentation.

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 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.

MILES is a multilingual text simplifier inspired by LSBert - A BERT-based lexical simplification approach proposed in 2018. Unlike LSBert, MILES uses the bert-base-multilingual-uncased model, as well as simple language-agnostic approaches to complex word identification (CWI) and candidate ranking.

Türkçe küfürlü içerikleri bulan bir yapay zeka kütüphanesi / An ML library for profanity detection in Turkish sentences

Python package for Turkish Language.

Turkish Stop Words Türkçe Dolgu Sözcükleri

Text to speech is a process to convert any text into voice. Text to speech project takes words on digital devices and convert them into audio. Here I have used Google-text-to-speech library popularly known as gTTS library to convert text file to .mp3 file. Hope you like my project!