Instructions to use CleveGreen/FieldClassifier_v2_gpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CleveGreen/FieldClassifier_v2_gpt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CleveGreen/FieldClassifier_v2_gpt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CleveGreen/FieldClassifier_v2_gpt") model = AutoModelForSequenceClassification.from_pretrained("CleveGreen/FieldClassifier_v2_gpt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from CleveGreen/FieldClassifier_v2_gpt: direct link, hf CLI and curl.
- Browser
- Download file 510 MB
-
https://huggingface.co/CleveGreen/FieldClassifier_v2_gpt/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://CleveGreen/FieldClassifier_v2_gpt/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/CleveGreen/FieldClassifier_v2_gpt/resolve/main/pytorch_model.bin
510 MB
- Xet hash:
- de33f8e46200045b03d510671764a6e136c8d7fbf30c514a8794c02afc154e12
- Size of remote file:
- 510 MB
- SHA256:
- 819e5177ffc2da4de070f42a5ae54c7896590f6bd14a9c20c81f392495268c97
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