torchtext
Natural Language Processing utilities for PyTorch (Legacy). Includes tokenizers, vocabulary building, and DataPipe-based dataset handling for text processing pipelines. (torchtext, tokenizer, vocab, datapipe, regextokenizer, nlp-pipeline)
npx skills add majiayu000/claude-skill-registry --skill torchtext --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.
## Overview TorchText is a legacy library for NLP in PyTorch. While it is in a maintenance phase, it remains a common tool for handling classic NLP datasets and building vocabularies via DataPipes. ## When to Use Use TorchText for maintaining legacy NLP projects or when utilizing its built-in DataPipe-based datasets. For new projects, transitioning to native PyTorch or other modern NLP libraries is recommended. ## Decision Tree 1. Are you starting a new NLP project? - CONSIDER: Using Hugging Face or native PyTorch instead of TorchText. 2. Do you need a high-performance tokenizer for production? - USE: `RegexTokenizer` and compile it with `torch.jit.script`. 3. Are you using DataPipes with multiple workers? - ENSURE: Use a proper `worker_init_fn` in the `DataLoader` to avoid data duplication. ## Workflows 1. **Building a Text Processing Pipeline** 1. Initialize a tokenizer (e.g., `BERTTokenizer`). 2. Construct a `Vocab` object using `build_vocab_from_iterator` from a dataset. 3. Create a pipeline using `transforms.Sequential` containing: Tokenizer -> VocabTransform -> AddToken -> Truncate -> ToTensor. 4. Pass raw strings through the pipeline to get padded tensors. 2. **Using Built-i
- Overview
- When to Use
- Decision Tree
- Workflows
- Non-Obvious Insights
- Evidence
- Scripts
- Dependencies
- References
What does the torchtext skill do?
Natural Language Processing utilities for PyTorch (Legacy). Includes tokenizers, vocabulary building, and DataPipe-based dataset handling for text processing pipelines. (torchtext, tokenizer, vocab, datapipe, regextokenizer, nlp-pipeline)
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill torchtext --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 majiayu000/claude-skill-registry, a repository with 534 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.