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 training_args.bin from James-kc-min/F_Roberta_classifier2: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/James-kc-min/F_Roberta_classifier2/resolve/main/training_args.bin
- Command line
-
hf download hf://James-kc-min/F_Roberta_classifier2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/James-kc-min/F_Roberta_classifier2/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- 4600fe3403455fa792ae93ac14cf34dd17d8b1ccfd84a8da5e8fee9d87f9b742
- Size of remote file:
- 3.06 kB
- SHA256:
- f519f6641030650600c0041f3a8355485f7af0f62a5b51b1aa4083cf9aa88a0a
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