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 from transformers import pipeline pipe = pipeline("question-answering", model="Chetna19/m_bert_base_qa_model")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29fda3a0285787117eb2a053865384c495e1d6d0dfa8630c4ed1e3f7d129a057
- Size of remote file:
- 431 MB
- SHA256:
- e3e2be74a28508707a54314115596a1d4775d5e8dce8698754fc4a7ccc3e8564
路
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