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.