Instructions to use sp-embraceable/Colbert-Reranker-FT-1500steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sp-embraceable/Colbert-Reranker-FT-1500steps with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sp-embraceable/Colbert-Reranker-FT-1500steps", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sp-embraceable/Colbert-Reranker-FT-1500steps") model = AutoModelForSequenceClassification.from_pretrained("sp-embraceable/Colbert-Reranker-FT-1500steps", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a8497bbd785679fa717f831a3b6ad09142114d607683568dc620869463380786
- Size of remote file:
- 2.27 GB
- SHA256:
- 0b8ffda444fcc916abb7fed1a730ec80dbba6bf5ecbccacf54a6286cf7ffb423
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