Instructions to use joniponi/multilabel_inpatient_comments with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joniponi/multilabel_inpatient_comments with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="joniponi/multilabel_inpatient_comments")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("joniponi/multilabel_inpatient_comments") model = AutoModelForSequenceClassification.from_pretrained("joniponi/multilabel_inpatient_comments", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from joniponi/multilabel_inpatient_comments: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/joniponi/multilabel_inpatient_comments/resolve/main/training_args.bin
- Command line
-
hf download hf://joniponi/multilabel_inpatient_comments/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/joniponi/multilabel_inpatient_comments/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- 62abe672564aac0c1a803ff9bd836d6a9369dbd52e8e1c79dcbe08b78f34927c
- Size of remote file:
- 3.06 kB
- SHA256:
- 7c8686e09f7b5283021c0c156a4edcc233e6d025a0b06950178a252d2c0c0178
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