Instructions to use CleveGreen/JobClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CleveGreen/JobClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CleveGreen/JobClassifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CleveGreen/JobClassifier") model = AutoModelForSequenceClassification.from_pretrained("CleveGreen/JobClassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from CleveGreen/JobClassifier: direct link, hf CLI and curl.
- Browser
- Download file 434 MB
-
https://huggingface.co/CleveGreen/JobClassifier/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://CleveGreen/JobClassifier/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/CleveGreen/JobClassifier/resolve/main/pytorch_model.bin
434 MB
- Xet hash:
- 5577f499e7ca57b96b69ee1837c05e0727aa8af01e32211e0558b40cb0a2bc2d
- Size of remote file:
- 434 MB
- SHA256:
- d5bcb91f728065217c490e36b5f91907b1f161c22a56490720363c437b0009a0
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