Instructions to use facebook/contriever-msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/contriever-msmarco with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="facebook/contriever-msmarco")# Load model directly from transformers import AutoTokenizer, Contriever tokenizer = AutoTokenizer.from_pretrained("facebook/contriever-msmarco") model = Contriever.from_pretrained("facebook/contriever-msmarco") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit ·
abe8c14
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Parent(s): 9fb3bc5
Add yaml metadata necessary for use with pipelines (#1)
Browse files- Add yaml metadata necessary for use with pipelines (996d4803dc62ac53fba994c8484726fe90a26a03)
Co-authored-by: Spencer Braun <spencer@users.noreply.huggingface.co>
README.md
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This model is the finetuned version of the pre-trained contriever model available here https://huggingface.co/facebook/contriever, following the approach described in [Towards Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118). The associated GitHub repository is available here https://github.com/facebookresearch/contriever.
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## Usage (HuggingFace Transformers)
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---
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tags:
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- feature-extraction
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pipeline_tag: feature-extraction
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---
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This model is the finetuned version of the pre-trained contriever model available here https://huggingface.co/facebook/contriever, following the approach described in [Towards Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118). The associated GitHub repository is available here https://github.com/facebookresearch/contriever.
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## Usage (HuggingFace Transformers)
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