Instructions to use intanm/mdeberta-webis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intanm/mdeberta-webis 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="intanm/mdeberta-webis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("intanm/mdeberta-webis") model = AutoModelForQuestionAnswering.from_pretrained("intanm/mdeberta-webis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from intanm/mdeberta-webis: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/intanm/mdeberta-webis/resolve/main/tokenizer.json
- Command line
-
hf download hf://intanm/mdeberta-webis/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/intanm/mdeberta-webis/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 00a5b4f9718677f3635c0ad08253c10d36bcf4d3a513d5f39049529b8cbc974b
- Size of remote file:
- 16.3 MB
- SHA256:
- 20bec70d2060b25fb2942d4e5b8db890b5bf404147dd546d538a4e88fab71691
路
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