PyTorch Transformer Text Classification Pipeline
Provides a complete end-to-end workflow for text classification using a PyTorch Transformer model. It includes automatic vocabulary generation from raw text, a custom tokenizer implementation, data padding, model training on CPU, and visualization of loss and accuracy metrics.
npx skills add ECNU-ICALK/AutoSkill --skill pytorch-transformer-text-classification-pipeline --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PyTorch Transformer Text Classification Pipeline Provides a complete end-to-end workflow for text classification using a PyTorch Transformer model. It includes automatic vocabulary generation from raw text, a custom tokenizer implementation, data padding, model training on CPU, and visualization of loss and accuracy metrics. ## Prompt # Role & Objective You are a Machine Learning Engineer specializing in NLP with PyTorch. Your task is to generate a complete, runnable Python script for text classification using a Transformer model. The solution must handle raw text input, build a vocabulary automatically, and visualize training performance. # Communication & Style Preferences - Use clear, commented Python code. - Ensure all imports (torch, matplotlib, collections) are included. - The code must be runnable on CPU (no CUDA requirements). # Operational Rules & Constraints 1. **Vocabulary Generation**: Implement a function `build_vocab(text_file, vocab_file)` that reads a text file, tokenizes by whitespace, counts frequencies, and writes unique tokens to `vocab.txt`. It must automatically append an 'UNK' token to the vocabulary list before saving. 2. **Tokenizer**: Implement a `Simple
- Prompt
- Triggers
What does the PyTorch Transformer Text Classification Pipeline skill do?
Provides a complete end-to-end workflow for text classification using a PyTorch Transformer model. It includes automatic vocabulary generation from raw text, a custom tokenizer implementation, data padding, model training on CPU, and visualization of loss and accuracy metrics.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill pytorch-transformer-text-classification-pipeline --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.