bert-pytorch

Files (16)

• SKILL.md
• api-reference.md · references/
• cli-reference.md · references/
• repo-provenance.md · references/
• repo-routing-metadata.json · references/
• troubleshooting.md · references/
• check_install.py · scripts/
• make_tiny_corpus.py · scripts/
• SKILL.md · sub-skills/data-preparation/
• corpus-format.md · sub-skills/data-preparation/references/
• troubleshooting.md · sub-skills/data-preparation/references/
• build_vocab_smoke.py · sub-skills/data-preparation/scripts/
• SKILL.md · sub-skills/training/
• training-workflow.md · sub-skills/training/references/
• troubleshooting.md · sub-skills/training/references/
• train_smoke.py · sub-skills/training/scripts/

SKILL.md (first lines)

# bert-pytorch

Use this skill when the user mentions BERT-pytorch, `bert`, `bert-vocab`, `WordVocab`, `BERTDataset`, `BERT`, `BERTLM`, or `BERTTrainer`.

## Quick start

1. Install the package with `python -m pip install bert-pytorch` if it is not already available in your Python environment.
2. Run `python scripts/check_install.py` to confirm the package import, public exports, and console commands.
3. If the task is about corpus layout, vocabulary files, or dataset rows, go to `sub-skills/data-preparation/SKILL.md`.
4. If the task is about model construction, training, device choice, or checkpoints, go to `sub-skills/training/SKILL.md`.
5. Use `python scripts/make_tiny_corpus.py --output /tmp/bert-pytorch-corpus.txt` when you need a stable two-line fixture.

## Route map

- `sub-skills/data-preparation/SKILL.md`: build or load vocabularies, validate corpus format, inspect `BERTDataset`, and diagnose pickle or row-format problems.
- `sub-skills/training/SKILL.md`: instantiate `BERT`, `BERTLM`, and `BERTTrainer`; train on a tiny corpus/vocab pair; pick CPU or CUDA; and save checkpoints.

## Shared facts

- The corpus format is two sentences per line separated by a tab.
- Tokenization happens before the package; BERT-pytorch does not tokenize raw text for you.
- `bert-vocab` and `bert` are the documented console commands.
- `BERT` hidden size must be divisible by the number of attention heads.
- The `bert` CLI parses some boolean flags with `type=bool`; use the bundled smoke scripts or the Python API when you need an explicit CPU run or streaming mode.

## Shared references

- `references/repo-provenance.md`: source commit, package version, and evidence snapshot.
- `references/repo-routing-metadata.json`: router metadata for managed import.
- `references/api-reference.md`: public classes, functions, and object relationships.
- `references/cli-reference.md`: command names, flags, defaults, and safety notes.
- `references/troubleshooting.md`: cross-cutting install, import, CLI, corpus, and device issues.

## Shared scripts

- `scripts/check_install.py`: verify imports, API exports, and CLI help.
- `scripts/make_tiny_corpus.py`: create a deterministic two-line corpus fixture.

## When in doubt

- Start with the sub-skill that matches the first concrete noun in the request: corpus/vocab/data rows -> data preparation; model/training/checkpoint/device -> training.
- If the task mixes both, build the vocabulary first, then move to training.
- Read `references/troubleshooting.md` before guessing about missing files, malformed rows, or device failures.

From VectorSpaceLab/AREX-Skill, Apache-2.0.

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