BERT: Sentence Classification on CoLA Dataset

GitHub: https://github.com/rukshar69/Transformers/blob/master/SentenceClassificationTransformersCoLA.ipynb Here, we use BERT((Bidirectional Encoder Representations from Transformers)) with the huggingface PyTorch library to efficiently fine-tune a model to acquire approximately state of the art performance in a single-sentence classification task on CoLA(The Corpus of Linguistic Acceptability) dataset. In other words, we apply transfer learning in NLP to make high performance models with

Sentiment Analysis with Transformers

Github Link: https://github.com/rukshar69/Transformers/blob/master/rukshar_transformer_sentiment_analysis.ipynb Here, we fine-tune a BERT Machine Learning model to build a Sentiment Classifier using Google Play app reviews dataset and the Transformers library by Hugging Face! A glimpse at the dataset. Here we use the ‘content’ and the ‘score’ column for sentiment analysis. The dataset’s shape is: (15746, 11) meaning, it has

PyTorch Transformers and Machine Translation

Here, I use PyTorch’s famous Transformers library to translate German sentence into English sentences. The model is trained on PyTorch’s Multi30k dataset. Github Link: https://github.com/rukshar69/Transformers/blob/master/pytorch_transformers_machine_translation.ipynb The model and training parameters are laid out just before the loop codes for training iterations. So, others can easily manipulate the models during their own training. I record the

Transformers from Scratch

Transformer Machine Learning Model from Scratch