Instructions to use cmarkea/bloomz-560m-reranking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cmarkea/bloomz-560m-reranking with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cmarkea/bloomz-560m-reranking") model = AutoModelForSequenceClassification.from_pretrained("cmarkea/bloomz-560m-reranking", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 490525f44b965542dd3c3cab9eb0df61e080bac82de9aa1fed53e6894a958ff1
- Size of remote file:
- 1.12 GB
- SHA256:
- b8409c77b6cf4bbf676808eee1dc6f7ddfcf82066f70e8a27bcf9aa68110db60
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