Instructions to use NeuML/bert-small-cord19qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NeuML/bert-small-cord19qa 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="NeuML/bert-small-cord19qa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("NeuML/bert-small-cord19qa") model = AutoModelForQuestionAnswering.from_pretrained("NeuML/bert-small-cord19qa", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NeuML/bert-small-cord19qa: direct link, hf CLI and curl.
- Browser
- Download file 139 MB
-
https://huggingface.co/NeuML/bert-small-cord19qa/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NeuML/bert-small-cord19qa/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NeuML/bert-small-cord19qa/resolve/main/flax_model.msgpack
139 MB
- Xet hash:
- a1774da1e2fcc3e1d4df0c0889b741e637854d56e83d6861d5e978919a90f90e
- Size of remote file:
- 139 MB
- SHA256:
- 87448c8a96d3465b53b54527b56d2555914589ce948caf92584072fc5718050d
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