Instructions to use roshnir/mBert-finetuned-mlqa-dev-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roshnir/mBert-finetuned-mlqa-dev-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="roshnir/mBert-finetuned-mlqa-dev-en")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("roshnir/mBert-finetuned-mlqa-dev-en") model = AutoModelForQuestionAnswering.from_pretrained("roshnir/mBert-finetuned-mlqa-dev-en") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 871dedd6b9e38b7b6c897bc035b48b445f576e4ac14f002b61254a651f02a846
- Size of remote file:
- 667 MB
- SHA256:
- f44325dc06fd81e2df5142e533e5b1f86fd90903eb0f816040bb3d353301abbb
路
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