Instructions to use PrimeQA/MITQA_OTTQA_DPR_Table_Retriever_Query_Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PrimeQA/MITQA_OTTQA_DPR_Table_Retriever_Query_Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="PrimeQA/MITQA_OTTQA_DPR_Table_Retriever_Query_Encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("PrimeQA/MITQA_OTTQA_DPR_Table_Retriever_Query_Encoder") model = AutoModel.from_pretrained("PrimeQA/MITQA_OTTQA_DPR_Table_Retriever_Query_Encoder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8fac1b741519a6b8183a15969b83f143acd0c5799e12deef665d471d7dd01e58
- Size of remote file:
- 436 MB
- SHA256:
- 3a16f1a427e60e3268b85141ebdd9033e000a4f9278b6c5bae1184c3affe1827
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