Instructions to use macavaney/deepct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use macavaney/deepct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="macavaney/deepct")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("macavaney/deepct") model = AutoModelForTokenClassification.from_pretrained("macavaney/deepct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from macavaney/deepct: direct link, hf CLI and curl.
- Browser
- Download file 809 Bytes
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https://huggingface.co/macavaney/deepct/resolve/main/README.md
- Command line
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hf download hf://macavaney/deepct/README.md
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curl -L -o README.md https://huggingface.co/macavaney/deepct/resolve/main/README.md
809 Bytes
metadata
language:
- en
tags:
- retrieval
- document-rewriting
datasets:
- irds:msmarco-passage
library_name: transformers
A DeepCT model based on bert-base-uncased and trained on MS MARCO. This is a version of the checkpoint released by the original authors, converted to pytorch format and ready for use in PyTerrier.
References
- [Dai19]: Zhuyun Dai, Jamie Callan. Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval. https://arxiv.org/abs/1910.10687
- [Macdonald20]: Craig Macdonald, Nicola Tonellotto. Declarative Experimentation in Information Retrieval using PyTerrier. Craig Macdonald and Nicola Tonellotto. In Proceedings of ICTIR 2020. https://arxiv.org/abs/2007.14271