Instructions to use dmrau/crossencoder-msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmrau/crossencoder-msmarco with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dmrau/crossencoder-msmarco")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dmrau/crossencoder-msmarco") model = AutoModelForSequenceClassification.from_pretrained("dmrau/crossencoder-msmarco") - Notebooks
- Google Colab
- Kaggle
Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers`
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by tomaarsen HF Staff - opened
README.md
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This model was trained with the [Rankers Library](`https://github.com/davidmrau/rankers`) please check https://github.com/davidmrau/rankers#Evaluation to learn how to use the model.
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This Cross Encoder Model was trained on the official MS MARCO training triples using the following training parameters:
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library_name: sentence-transformers
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pipeline_tag: text-ranking
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---
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This model was trained with the [Rankers Library](`https://github.com/davidmrau/rankers`) please check https://github.com/davidmrau/rankers#Evaluation to learn how to use the model.
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This Cross Encoder Model was trained on the official MS MARCO training triples using the following training parameters:
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