Instructions to use deepset/xlm-roberta-base-squad2-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/xlm-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/xlm-roberta-base-squad2-distilled")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/xlm-roberta-base-squad2-distilled") model = AutoModelForQuestionAnswering.from_pretrained("deepset/xlm-roberta-base-squad2-distilled", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from deepset/xlm-roberta-base-squad2-distilled: direct link, hf CLI and curl.
- Browser
- Download file 9.08 MB
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https://huggingface.co/deepset/xlm-roberta-base-squad2-distilled/resolve/main/tokenizer.json
- Command line
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hf download hf://deepset/xlm-roberta-base-squad2-distilled/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/deepset/xlm-roberta-base-squad2-distilled/resolve/main/tokenizer.json
9.08 MB
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