Instructions to use Ridhwan/locum_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ridhwan/locum_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ridhwan/locum_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ridhwan/locum_classification") model = AutoModelForSequenceClassification.from_pretrained("Ridhwan/locum_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ridhwan/locum_classification: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Ridhwan/locum_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ridhwan/locum_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ridhwan/locum_classification/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 3b95488f65b70d30fd8bef961e897009bc3e329cf26e14be2172fae95b9d08de
- Size of remote file:
- 268 MB
- SHA256:
- a945b999714e0e89dded3b2f0a1e6a60565323eb4be89340c53ba1fd035f7ea5
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