Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use netaicsco/v1.2_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use netaicsco/v1.2_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="netaicsco/v1.2_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("netaicsco/v1.2_classifier") model = AutoModelForSequenceClassification.from_pretrained("netaicsco/v1.2_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from netaicsco/v1.2_classifier: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/netaicsco/v1.2_classifier/resolve/main/training_args.bin
- Command line
-
hf download hf://netaicsco/v1.2_classifier/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/netaicsco/v1.2_classifier/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 1fa50a5137686b84d7ffec8f6f64ed8e034949d03fc8a20503923cdc0452ac57
- Size of remote file:
- 5.3 kB
- SHA256:
- 19b21a08a9c935864a94433139c0695ede7954f28273de5d51d3bab44bca7bd5
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