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:
- 2fb2fefe4d69a90c176bc0901da9dd44f803fdf2c3f49bb0d5e4a924d79430fc
- Size of remote file:
- 436 MB
- SHA256:
- ff676ab1d221c842f711123c8f5664921762d2ebd0666421a210c9fbd0ab34a0
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