Instructions to use deepset/xlm-roberta-base-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/xlm-roberta-base-squad2 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/xlm-roberta-base-squad2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/xlm-roberta-base-squad2") model = AutoModelForQuestionAnswering.from_pretrained("deepset/xlm-roberta-base-squad2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Add evaluation results on the plain_text config of squad
Beep boop, I am a bot from Hugging Face's automatic model evaluator 👋!\ Your model has been evaluated on the plain_text config of the [squad](https://huggingface.co/datasets/squad) dataset by @sakamoto, using the predictions stored [here](https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad-d38f255e-13865909).\ Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=squad).\ Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/model-evaluator?dataset=squad).8ff79d8
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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 (9307bcfe5042a1339aec0e091f58b69a8fc5c05a) Co-authored-by: Evaluation Bot <autoevaluator@users.noreply.huggingface.co>8962e17
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