Fine-Tuning BERT with HuggingFace and PyTorch Lightning for Multilabel Text Classification | Train

Learn how to create a model that uses BERT to classify toxic comments. Use PyTorch Lightning to train and evaluate it. We’ll look at the history of the training progress using TensorBoard!

In the end, we’ll build a simple function that ties everything together and classifies toxic text.

🔣 GitHub: https://github.com/curiousily/Getting… ​

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Fine-Tuning BERT with HuggingFace and PyTorch Lightning for Multilabel Text Classification | Train

Learn how to create a model that uses BERT to classify toxic comments. Use PyTorch Lightning to train and evaluate it. We’ll look at the history of the training progress using TensorBoard!

In the end, we’ll build a simple function that ties everything together and classifies toxic text.

🔣 GitHub: https://github.com/curiousily/Getting… ​

Subscribe: https://www.youtube.com/c/VenelinValkovBG/featured

#pytorch #python

Fine-Tuning BERT with HuggingFace and PyTorch Lightning for Multilabel Text Classification | Dataset

Learn how to use BERT to classify toxic comments from raw text. You’ll learn how to prepare a custom dataset, tokenize the text using the Transformers library by HuggingFace. We’ll have a look at PyTorch Lightning and create a data module for our dataset.

🔣 GitHub: https://github.com/curiousily/Getting… ​

Subscribe: https://www.youtube.com/c/VenelinValkovBG/featured

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Fine-tuning BERT and RoBERTa for high accuracy text classification in PyTorch

As of the time of writing this piece, state-of-the-art results on NLP and NLU tasks are obtained with Transformer models. There is a trend of performance improvement as models become deeper and larger, GPT 3 comes to mind. Training small versions of such models from scratch takes a significant amount of time, even with GPU. This problem can be solved via pre-training when a model is trained on a large text corpus using a high-performance cluster. Later it can be fine-tuned for a specific task in a much shorter amount of time. During fine tuning stage, additional layers can be added to the model for specific tasks, which can be different from those for which the model was initially trained. This technique is related to transfer learning, a concept applied to areas of machine learning beyond NLP (see here and here for a quick intro).

In this post, I would like to share my experience of fine-tuning BERT and RoBERTa, available from the transformers library by Hugging Face, for a document classification task. Both models share a transformer architecture, which consists of at least two distinct blocks — encoder and decoder. Both encoder and decoder consist of multiple layers based around Attention mechanism. Encoder processed the input token sequence into a vector of floating point numbers — a hidden state, which is picked up by the decoder. It is the hidden state that encompasses the information content of the input sequence. This enables to represent an entire sequence of tokens with a single dense vector of float point numbers. Two texts or documents, which have similar meaning are represented by closely aligned vectors. Comparing vectors using a metric of choice, for example, cosine similarity, enables to quantify the similarity of original text pieces.

#machine-learning #nlp #data-science #text-classification #pytorch

How to fine-tune BERT on text classification task?

BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based architecture released in the paper “Attention Is All You Need**_” in the year 2016 by Google. The BERT model got published in the year 2019 in the paper — “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”. _**When it was released, it showed the state of the art results on GLUE benchmark.

Introduction

First, I will tell a little bit about the Bert architecture, and then will move on to the code on how to use is for the text classification task.

The BERT architecture is a multi-layer bidirectional transformer’s encoder described in the paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

There are two different architecture’s proposed in the paper. **BERT_base **and **BERT_large. **The BERT base architecture has L=12, H=768, A=12 and a total of around 110M parameters. Here L refers to the number of transformer blocks, H refers to the hidden size, A refers to the number of self-attention head. For BERT large, L=24, H=1024, A=16.

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Source:- https://www.kdnuggets.com/2018/12/bert-sota-nlp-model-explained.html

The input format of the BERT is given in the above image. I won’t get into much detail into this. You can refer the above link for a more detailed explanation.

Source Code

The code which I will be following can be cloned from the following HuggingFace’s GitHub repo -

https://github.com/huggingface/transformers/

Scripts to be used

Majorly we will be modifying and using two scripts for our text classification task. One is **_glue.py, _**and the other will be **_run_glue.py. _**The file glue.py path is “_transformers/data/processors/” _and the file run_glue.py can be found in the location “examples/text-classification/”.

#deep-learning #machine-learning #text-classification #bert #nlp #deep learning

Text Classification With PyTorch

A baseline model with LSTMs

The question remains open: how to learn semantics? what is semantics? would DL-based models be capable to learn semantics?

Introduction

The aim of this blog is to explain how to build a text classifier based on LSTMs as well as how it is built by using the PyTorch framework.

I would like to start with the following question: how to classify a text? Several approaches have been proposed from different viewpoints under different premises, but what is the most suitable one?. It’s interesting to pause for a moment and question ourselves: how we as humans can classify a text?, what do our brains take into account to be able to classify a text?. Such questions are complex to be answered.

Currently, we have access to a set of different text types such as emails, movie reviews, social media, books, etc. In this sense, the text classification problem would be determined by what’s intended to be classified (e.g. _is it intended to classify the polarity of given text? Is it intended to classify a set of movie reviews by category? Is it intended to classify a set of texts by topic? _). In this regard, the problem of text classification is categorized most of the time under the following tasks:

• Sentiment analysis
• News categorization
• Topic analysis
• Question answering
• Natural language inference

In order to go deeper into this hot topic, I really recommend to take a look at this paper: Deep Learning Based Text Classification: A Comprehensive Review.

Methodology

The two keys in this model are: tokenization and recurrent neural nets. Tokenization refers to the process of splitting a text into a set of sentences or words (i.e. tokens). In this regard, tokenization techniques can be applied at sequence-level or word-level. In order to understand the bases of tokenization you can take a look at: Introduction to Information Retrieval.

In the other hand, RNNs (Recurrent Neural Networks) are a kind of neural network which are well-known to work well on sequential data, such as the case of text data. In this case, it’s been implemented a special kind of RNN which is LSTMs (Long-Short Term Memory). LSTMs are one of the _improved _versions of RNNs, essentially LSTMs have shown a better performance working with _longer sentences. _In order to go deeper about what RNNs and LSTMs are, you can take a look at: Understanding LSTMs Networks.

Since the idea of this blog is to present a baseline model for text classification, the text preprocessing phase is based on the tokenization technique, meaning that each _text sentence _will be tokenized, then each _token _will be transformed into its index-based representation. Then, each _token sentence based indexes _will be passed sequentially through an embedding layer, this embedding layer will output an embedded representation of each token whose are passed through a two-stacked LSTM neural net, then the last LSTM’s hidden state will be passed through a two-linear layer neural net which outputs a single value filtered by a sigmoid activation function. The following image describes the model architecture:

#pytorch #text-mining #lstm #text-classification #python