https://github.com/anonymous-leviathan/handling-multiple-sequences-pytorch

This code uses HuggingFace's DistilBERT model to tokenize a sentence, convert it to IDs, and pass them to the model to get classification scores (logits). It shows how a sequence is processed for sentiment analysis using PyTorch.
https://github.com/anonymous-leviathan/handling-multiple-sequences-pytorch

huggingface huggingface-transformers python3 pytorch-implementation self-learning-project

Last synced: about 2 months ago
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This code uses HuggingFace's DistilBERT model to tokenize a sentence, convert it to IDs, and pass them to the model to get classification scores (logits). It shows how a sequence is processed for sentiment analysis using PyTorch.

• Host: GitHub
• URL: https://github.com/anonymous-leviathan/handling-multiple-sequences-pytorch
• Owner: anonymous-leviathan
• Created: 2024-10-13T13:32:00.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-10-13T14:08:34.000Z (3 months ago)
• Last Synced: 2024-10-27T10:25:35.163Z (3 months ago)
• Topics: huggingface, huggingface-transformers, python3, pytorch-implementation, self-learning-project
• Language: Jupyter Notebook
• Homepage:
• Size: 10.7 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
This code loads a pre-trained DistilBERT model from HuggingFace for sequence classification. It tokenizes a given text sequence, converts the tokens into numerical IDs, and passes these IDs into the model to get classification results (logits). The steps are as follows:

Load Libraries and Model: The torch library and HuggingFace's AutoTokenizer and AutoModelForSequenceClassification are imported. A pre-trained model checkpoint for sentiment analysis (distilbert-base-uncased-finetuned-sst-2-english) is loaded.

Tokenization: The sentence is tokenized (split into meaningful parts), and the tokens are converted into IDs that the model can understand.

Model Inference: The input IDs are fed into the model to generate the output logits (raw prediction scores).

You can use this explanation in your GitHub project description for clarity.