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 pytorch_model.bin from joniponi/multilabel_inpatient_comments: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/joniponi/multilabel_inpatient_comments/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://joniponi/multilabel_inpatient_comments/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/joniponi/multilabel_inpatient_comments/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- e95fa60d3e13ffb93512bf69ada4f3683d8d65f8bc8ac1fdda96fef775b5cab7
- Size of remote file:
- 433 MB
- SHA256:
- 756dca16e49156d2cdf4ac9c6ffc715916997fa758f3593e92698d99e139b4ad
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