Switch to XLM-RoBERTa for Asian language support
Nobody has claimed this yet.
- Dominant language
- Go
- Stars
- 432
- Forks
- 51
- PR merge metrics
- No merged PRs in 30d
Description
Motivation
To improve support for Asian languages, we should switch our current model to XLM-RoBERTa.
XLM-RoBERTa is a multilingual model pre-trained on 100 languages, including a wide range of Asian languages (Chinese, Japanese, Korean, Thai, Vietnamese, Hindi, etc.), and consistently outperforms multilingual BERT (mBERT) on cross-lingual benchmarks.
Proposal
- Evaluate XLM-RoBERTa (
xlm-roberta-baseand/orxlm-roberta-large) as a replacement for the current model. - Benchmark performance on Asian language inputs against the current setup.
- Update the model loading / inference code paths to support XLM-RoBERTa.
- Update documentation and any related configs.
References
- Hugging Face docs: https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by locating the current model loading and inference code paths and related configs, then read how the existing model is benchmarked on language inputs. Evaluate xlm-roberta-base and/or xlm-roberta-large against the current setup. Done means benchmark results are available and the selected model is supported in inference, documentation, and related configs.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- go, huggingface
- Domain
- internationalization, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100