Instructions to use Chetna19/m_bert_base_qa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chetna19/m_bert_base_qa_model 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="Chetna19/m_bert_base_qa_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Chetna19/m_bert_base_qa_model") model = AutoModelForQuestionAnswering.from_pretrained("Chetna19/m_bert_base_qa_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Chetna19/m_bert_base_qa_model: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/Chetna19/m_bert_base_qa_model/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://Chetna19/m_bert_base_qa_model@refs/pr/1/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/Chetna19/m_bert_base_qa_model/resolve/refs%2Fpr%2F1/training_args.bin
3.58 kB
- Xet hash:
- 9e5808189b05635df2ede908896e6d3a50633deb101e351f1b725a9db08a90e1
- Size of remote file:
- 3.58 kB
- SHA256:
- 8b2bba241b62c72aca17447db3eb26f4c112ccafb38ca52be4c6b87a9d4aeb82
路
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