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… ​

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#pytorch #python

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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

#pytorch #python

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 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

Hands-on Guide to Pattern - A Python Tool for Effective Text Processing and Data Mining

Text Processing mainly requires Natural Language Processing( NLP), which is processing the data in a useful way so that the machine can understand the Human Language with the help of an application or product. Using NLP we can derive some information from the textual data such as sentiment, polarity, etc. which are useful in creating text processing based applications.

Python provides different open-source libraries or modules which are built on top of NLTK and helps in text processing using NLP functions. Different libraries have different functionalities that are used on data to gain meaningful results. One such Library is Pattern.

Pattern is an open-source python library and performs different NLP tasks. It is mostly used for text processing due to various functionalities it provides. Other than text processing Pattern is used for Data Mining i.e we can extract data from various sources such as Twitter, Google, etc. using the data mining functions provided by Pattern.

In this article, we will try and cover the following points:

• NLP Functionalities of Pattern
• Data Mining Using Pattern

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#developers corner #data mining #text analysis #text analytics #text classification #text dataset #text-based algorithm

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