Instructions to use moussaKam/frugalscore_small_deberta_bert-score with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moussaKam/frugalscore_small_deberta_bert-score with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="moussaKam/frugalscore_small_deberta_bert-score")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("moussaKam/frugalscore_small_deberta_bert-score") model = AutoModelForSequenceClassification.from_pretrained("moussaKam/frugalscore_small_deberta_bert-score", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from moussaKam/frugalscore_small_deberta_bert-score: direct link, hf CLI and curl.
- Browser
- Download file 115 MB
-
https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://moussaKam/frugalscore_small_deberta_bert-score/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score/resolve/main/pytorch_model.bin
115 MB
- Xet hash:
- 8b18527ccb79ddd88ec13f36e2af15a8c54970d6e6110ab69ac751b2d9cd520e
- Size of remote file:
- 115 MB
- SHA256:
- fb63d6308eb2e1ebb2bb61f9a5f8656b60926debdfe97c894a3948111fa3b333
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