Instructions to use castorini/monot5-large-msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use castorini/monot5-large-msmarco with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="castorini/monot5-large-msmarco")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("castorini/monot5-large-msmarco") model = AutoModelForMultimodalLM.from_pretrained("castorini/monot5-large-msmarco") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 72fc0e940261c524f069276dd204a39ce737d53aa9c375b6da9cea5c3cb0edbf
- Size of remote file:
- 2.95 GB
- SHA256:
- a6e63efb6811c938ac8495c957e3634cf657bc6ad0152de2bf4e1d919f74c6fc
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