Practical overview
When it fits
Use it when you already have or can build a transformer and want a compact MLM training wrapper for sequence batches.
Who it is for
- PyTorch developers adding masked-language-model pretraining to a transformer pipeline
- Researchers or engineers training transformer models from sequence corpora
Documented uses
- Set up masked-language-model training around a transformer.
- Configure masking, padding and ignored token IDs.
- Calculate batch loss and update model parameters with an optimizer.
- Save a trained transformer for downstream work.
Requirements & limits
The repository is public and lists an MIT license. The README documents installation with pip.
- You supply the transformer architecture and sequence batches; the library provides the MLM training wrapper.
- The example uses x-transformers for its model and PyTorch Adam for optimization.
- Mask, padding and ignored-token IDs must correspond to the data's tokenization.
- The README demonstrates CUDA placement and a training step, rather than a complete data or evaluation pipeline.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up one masked-language-model training step
Configure the documented trainer and perform one optimizer update on a batch of sequence data.
Before you start
- Python environment with PyTorch
- Install mlm-pytorch and x-transformers
- A transformer model and tokenized sequence batches
- Token IDs for masking and padding, plus any tokens to exclude from masking
- CUDA for the README's shown device-placement example
Steps
Install mlm-pytorch with pip and install x-transformers.
Create a transformer, as in the README's x-transformers encoder example.
Wrap the model with MLM, setting mask_token_id, pad_token_id, mask_prob, replace_prob and mask_ignore_token_ids for the tokenization scheme.
Pass a batch to the trainer, backpropagate the returned loss, then step and clear a PyTorch optimizer.
Expected outcome
One optimizer update using the MLM loss; the README shows repeating this process over batches and saving the transformer with torch.save.
Guide limitations
- The repository example uses CUDA and does not document CPU device placement.
- The example's token IDs, model dimensions, batch shape and masking values are illustrative configuration, not universal settings.
- The guide covers the training step, not corpus preparation or downstream evaluation.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- lucidrains/mlm-pytorch · Official homepage title · retained in its source language