Instructions to use CleveGreen/FieldClassifier_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CleveGreen/FieldClassifier_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CleveGreen/FieldClassifier_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CleveGreen/FieldClassifier_v2") model = AutoModelForSequenceClassification.from_pretrained("CleveGreen/FieldClassifier_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from CleveGreen/FieldClassifier_v2: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/CleveGreen/FieldClassifier_v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://CleveGreen/FieldClassifier_v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/CleveGreen/FieldClassifier_v2/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 88b10d6f7a84a872bf6f0520d3cbe0c07e3845489f65ec52d5bd9f0db130489c
- Size of remote file:
- 433 MB
- SHA256:
- 82c089b8a6614dc3a51cc77b4fe8f49a850264852324044a51fe0b2ba5e25542
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