Instructions to use CleveGreen/FieldClassifier_v3_gpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CleveGreen/FieldClassifier_v3_gpt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CleveGreen/FieldClassifier_v3_gpt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CleveGreen/FieldClassifier_v3_gpt") model = AutoModelForSequenceClassification.from_pretrained("CleveGreen/FieldClassifier_v3_gpt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from CleveGreen/FieldClassifier_v3_gpt: direct link, hf CLI and curl.
- Browser
- Download file 510 MB
-
https://huggingface.co/CleveGreen/FieldClassifier_v3_gpt/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://CleveGreen/FieldClassifier_v3_gpt/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/CleveGreen/FieldClassifier_v3_gpt/resolve/main/pytorch_model.bin
510 MB
- Xet hash:
- da466b08c5ef3b383f2098a42528465f51ce6662665b4b81165b0d3c1453c450
- Size of remote file:
- 510 MB
- SHA256:
- 9b4ebc9dd1fb3a50c4d41884d98bc3d4ccac0571d7e8a2931119ef5c2c400d8a
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