Instructions to use ancs21/xlm-roberta-large-vi-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ancs21/xlm-roberta-large-vi-qa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="ancs21/xlm-roberta-large-vi-qa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ancs21/xlm-roberta-large-vi-qa") model = AutoModelForQuestionAnswering.from_pretrained("ancs21/xlm-roberta-large-vi-qa") - Notebooks
- Google Colab
- Kaggle
XLM-RoBERTa large for QA on Vietnamese languages (also support various languages)
Overview
- Language model: xlm-roberta-large
- Fine-tune: deepset/xlm-roberta-large-squad2
- Language: Vietnamese
- Downstream-task: Extractive QA
- Dataset: mailong25/bert-vietnamese-question-answering
- Training data: train-v2.0.json (SQuAD 2.0 format)
- Eval data: dev-v2.0.json (SQuAD 2.0 format)
- Infrastructure: 1x Tesla P100 (Google Colab)
Performance
Evaluated on dev-v2.0.json
exact: 136 / 141
f1: 0.9692671394799054
Evaluated on Vietnamese XQuAD: xquad.vi.json
exact: 604 / 1190
f1: 0.7224454217571596
Author
An Pham (ancs21.ps [at] gmail.com)
License
MIT
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