Instructions to use deepset/roberta-base-squad2-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/roberta-base-squad2-distilled with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="deepset/roberta-base-squad2-distilled")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/roberta-base-squad2-distilled") model = AutoModelForQuestionAnswering.from_pretrained("deepset/roberta-base-squad2-distilled", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Add evaluation results on the adversarialQA config of adversarial_qa
Beep boop, I am a bot from Hugging Face's automatic model evaluator 👋!\ Your model has been evaluated on the adversarialQA config of the [adversarial_qa](https://huggingface.co/datasets/adversarial_qa) dataset by @ceyda, using the predictions stored [here](https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-adversarial_qa-e34332b7-12205626).\ Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=adversarial_qa).\ Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/model-evaluator?dataset=adversarial_qa).bfa1e43
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Add evaluation results on the squad_v2 config of squad_v2 (#1)
- Add evaluation results on the squad_v2 config of squad_v2 (c3a09fd85ab6cf3895873ae179a87cb87f31b1c0) Co-authored-by: Evaluation Bot <autoevaluator@users.noreply.huggingface.co>13d15d6
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