Instructions to use macavaney/monot5-base-msmarco-sim1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use macavaney/monot5-base-msmarco-sim1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("macavaney/monot5-base-msmarco-sim1") model = AutoModelForSeq2SeqLM.from_pretrained("macavaney/monot5-base-msmarco-sim1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 613462d7ba81542fe0f1dd538b4a5f1d8a3cb19ac7058a0498f1f87d9ff353dc
- Size of remote file:
- 892 MB
- SHA256:
- 7cc03f8159949db22f037dea1e03f67f34c5e5b6d59ddd2f7eafd22e9b9b69f9
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