Instructions to use castorini/monot5-base-med-msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use castorini/monot5-base-med-msmarco with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="castorini/monot5-base-med-msmarco")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("castorini/monot5-base-med-msmarco") model = AutoModel.from_pretrained("castorini/monot5-base-med-msmarco", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
This model is a T5-base reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epoch) and then fine-tuned again on MedMARCO (from Sledge-Z paper for 1k steps.
For more details on how to use it, check pygaggle.ai
Paper describing the model: Document Ranking with a Pretrained Sequence-to-Sequence Model
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