Instructions to use McGill-NLP/dpr-conversation_encoder-basic_info with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use McGill-NLP/dpr-conversation_encoder-basic_info with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="McGill-NLP/dpr-conversation_encoder-basic_info")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/dpr-conversation_encoder-basic_info") model = AutoModel.from_pretrained("McGill-NLP/dpr-conversation_encoder-basic_info") - Notebooks
- Google Colab
- Kaggle
Upload with huggingface_hub
Browse files- config.json +2 -2
- pytorch_model.bin +2 -2
config.json
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{
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"_name_or_path": "facebook/dpr-
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"architectures": [
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"
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"attention_probs_dropout_prob": 0.1,
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"gradient_checkpointing": false,
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{
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"_name_or_path": "facebook/dpr-question_encoder-single-nq-base",
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"architectures": [
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"DPRQuestionEncoder"
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"attention_probs_dropout_prob": 0.1,
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"gradient_checkpointing": false,
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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size 438022001
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