Instructions to use abehandlerorg/abstractclassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abehandlerorg/abstractclassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="abehandlerorg/abstractclassifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("abehandlerorg/abstractclassifier") model = AutoModelForSequenceClassification.from_pretrained("abehandlerorg/abstractclassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from abehandlerorg/abstractclassifier: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/abehandlerorg/abstractclassifier/resolve/main/model.safetensors
- Command line
-
hf download hf://abehandlerorg/abstractclassifier/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/abehandlerorg/abstractclassifier/resolve/main/model.safetensors
268 MB
- Xet hash:
- 83dec416195139d49d1d2ae8b4bd5998c0899dd97e0f0b1bb78987132865e649
- Size of remote file:
- 268 MB
- SHA256:
- 7972e29008bf48ae11051350c127d542d3dfdc6cb44801dd6ad04dbb14ba65d9
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