Instructions to use sp-embraceable/Colbert-Reranker-FT-2000steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sp-embraceable/Colbert-Reranker-FT-2000steps with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sp-embraceable/Colbert-Reranker-FT-2000steps")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sp-embraceable/Colbert-Reranker-FT-2000steps") model = AutoModelForSequenceClassification.from_pretrained("sp-embraceable/Colbert-Reranker-FT-2000steps") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cfb5640e76bbfb7e6c07beec307a86ef1e893b51d7cbceacc91ec7f998f06610
- Size of remote file:
- 17.1 MB
- SHA256:
- 8bf8afbfd11306bd872018c53bfdf2e160a56f8edbcf49933324404791c148d3
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