Instructions to use timpal0l/mdeberta-v3-base-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use timpal0l/mdeberta-v3-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="timpal0l/mdeberta-v3-base-squad2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("timpal0l/mdeberta-v3-base-squad2") model = AutoModelForQuestionAnswering.from_pretrained("timpal0l/mdeberta-v3-base-squad2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timpal0l/mdeberta-v3-base-squad2: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/timpal0l/mdeberta-v3-base-squad2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timpal0l/mdeberta-v3-base-squad2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timpal0l/mdeberta-v3-base-squad2/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- f889c44c06246aa08fd33ae13c2a8533c2b89984812e19bf99519276a5289f7f
- Size of remote file:
- 1.11 GB
- SHA256:
- 91d05e57e35a8a3768fbdbd26ecfa3c0672f6e889c0554e1859cabf282de2c56
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