Instructions to use wandb/WeaveContextRelevanceScorerV1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wandb/WeaveContextRelevanceScorerV1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="wandb/WeaveContextRelevanceScorerV1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wandb/WeaveContextRelevanceScorerV1") model = AutoModelForTokenClassification.from_pretrained("wandb/WeaveContextRelevanceScorerV1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e8f96f54b2384cb0b2b655d255d5c9d2beeb133fe01160684aa0c84a78bb246c
- Size of remote file:
- 5.56 kB
- SHA256:
- 4e31ec14b4094c9de8355dfadba6e90a4982f88fa3f80b3f5ac22e30399a1457
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