Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Umesh/pulf-classifier_roberta_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Umesh/pulf-classifier_roberta_final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Umesh/pulf-classifier_roberta_final")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Umesh/pulf-classifier_roberta_final") model = AutoModelForSequenceClassification.from_pretrained("Umesh/pulf-classifier_roberta_final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Umesh/pulf-classifier_roberta_final: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/Umesh/pulf-classifier_roberta_final/resolve/main/training_args.bin
- Command line
-
hf download hf://Umesh/pulf-classifier_roberta_final/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Umesh/pulf-classifier_roberta_final/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- fb8793575509600b501d7eb2deb73bfa66d8a340e49b6592b3f36c95b87bde46
- Size of remote file:
- 3.64 kB
- SHA256:
- 8c3cc75bbc056f1b89ae883f5df78baccc2209e5819a3c7c23a24118fd39df3b
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