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 pytorch_model.bin from Umesh/pulf-classifier_roberta_final: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Umesh/pulf-classifier_roberta_final/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Umesh/pulf-classifier_roberta_final@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Umesh/pulf-classifier_roberta_final/resolve/refs%2Fpr%2F1/pytorch_model.bin
499 MB
- Xet hash:
- f8d2d1deef3aea9124a9bc1a69e3d39c39afdd8d322715f0a5eb50dd874e4fc8
- Size of remote file:
- 499 MB
- SHA256:
- d626a536c81aa2025c499e9c26057676336cb434dabbbd3b1080331673c4033f
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