Instructions to use roshnir/bert-multi-uncased-trained-squadv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roshnir/bert-multi-uncased-trained-squadv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="roshnir/bert-multi-uncased-trained-squadv2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("roshnir/bert-multi-uncased-trained-squadv2") model = AutoModelForQuestionAnswering.from_pretrained("roshnir/bert-multi-uncased-trained-squadv2") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
mBERT base uncased model trained on 50% SQUAD data. This model can further be used to fine-tune using dev data for QA system on a specific language. The process is similar to what was followed in MLQA paper[https://aclanthology.org/2020.acl-main.421.pdf].
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