Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use James-kc-min/F_Roberta_classifier2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use James-kc-min/F_Roberta_classifier2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="James-kc-min/F_Roberta_classifier2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("James-kc-min/F_Roberta_classifier2") model = AutoModelForSequenceClassification.from_pretrained("James-kc-min/F_Roberta_classifier2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from James-kc-min/F_Roberta_classifier2: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/James-kc-min/F_Roberta_classifier2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://James-kc-min/F_Roberta_classifier2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/James-kc-min/F_Roberta_classifier2/resolve/main/pytorch_model.bin
329 MB
- Xet hash:
- e81a08b648fd9a7c4b34d0e9f4e883e986fce2eb97599036e2e32abd8ddcbe83
- Size of remote file:
- 329 MB
- SHA256:
- 57738f32a9d2d413152f34c3f6cbe0d33fab285eba9bcd172e250bfaf43147ed
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