Instructions to use prithivida/parrot_adequacy_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivida/parrot_adequacy_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prithivida/parrot_adequacy_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prithivida/parrot_adequacy_model") model = AutoModelForSequenceClassification.from_pretrained("prithivida/parrot_adequacy_model", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download training_args.bin from prithivida/parrot_adequacy_model: direct link, hf CLI and curl.
- Browser
- Download file 2.67 kB
-
https://huggingface.co/prithivida/parrot_adequacy_model/resolve/main/training_args.bin
- Command line
-
hf download hf://prithivida/parrot_adequacy_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/prithivida/parrot_adequacy_model/resolve/main/training_args.bin
2.67 kB
- Xet hash:
- 4ab3ff7107ce1bb8df72ae9eacc0bd8671bd32ab3b73a86e037c89d6280c6e54
- Size of remote file:
- 2.67 kB
- SHA256:
- 74ec09eb0f61301520bf91d941806c5c4da5292c38fec5dcb1ab76e31633cb05
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