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 pytorch_model.bin from Chetna19/m_bert_base_qa_model: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/Chetna19/m_bert_base_qa_model/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Chetna19/m_bert_base_qa_model@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Chetna19/m_bert_base_qa_model/resolve/refs%2Fpr%2F1/pytorch_model.bin
431 MB
- Xet hash:
- 29fda3a0285787117eb2a053865384c495e1d6d0dfa8630c4ed1e3f7d129a057
- Size of remote file:
- 431 MB
- SHA256:
- e3e2be74a28508707a54314115596a1d4775d5e8dce8698754fc4a7ccc3e8564
路
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