Instructions to use rseeto/multilabel_disease_bert_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use rseeto/multilabel_disease_bert_classification with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased") model = PeftModel.from_pretrained(base_model, "rseeto/multilabel_disease_bert_classification") - Transformers
How to use rseeto/multilabel_disease_bert_classification with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rseeto/multilabel_disease_bert_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sample_data/README.md from rseeto/multilabel_disease_bert_classification: direct link, hf CLI and curl.
- Browser
- Download file 962 Bytes
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https://huggingface.co/rseeto/multilabel_disease_bert_classification/resolve/main/sample_data/README.md
- Command line
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hf download hf://rseeto/multilabel_disease_bert_classification/sample_data/README.md
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curl -L -o README.md https://huggingface.co/rseeto/multilabel_disease_bert_classification/resolve/main/sample_data/README.md
This directory includes a few sample datasets to get you started.
california_housing_data*.csvis California housing data from the 1990 US Census; more information is available at: https://docs.google.com/document/d/e/2PACX-1vRhYtsvc5eOR2FWNCwaBiKL6suIOrxJig8LcSBbmCbyYsayia_DvPOOBlXZ4CAlQ5nlDD8kTaIDRwrN/pubmnist_*.csvis a small sample of the MNIST database, which is described at: http://yann.lecun.com/exdb/mnist/anscombe.jsoncontains a copy of Anscombe's quartet; it was originally described inAnscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American Statistician. 27 (1): 17-21. JSTOR 2682899.
and our copy was prepared by the vega_datasets library.