Instructions to use rockmiin/QMSum-dpr-query-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rockmiin/QMSum-dpr-query-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="rockmiin/QMSum-dpr-query-encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rockmiin/QMSum-dpr-query-encoder") model = AutoModel.from_pretrained("rockmiin/QMSum-dpr-query-encoder") - Notebooks
- Google Colab
- Kaggle
upload model
Browse files- pytorch_model.bin +3 -0
pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:0fe86b0973e2c0d24a443127ed7643da7c1d2d3218b9b2dd3f8e9a16a0c06c38
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size 90897137
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