Instructions to use Umesh/pulf-classifier_roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Umesh/pulf-classifier_roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Umesh/pulf-classifier_roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Umesh/pulf-classifier_roberta") model = AutoModelForSequenceClassification.from_pretrained("Umesh/pulf-classifier_roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Umesh/pulf-classifier_roberta: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/Umesh/pulf-classifier_roberta/resolve/main/training_args.bin
- Command line
-
hf download hf://Umesh/pulf-classifier_roberta/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Umesh/pulf-classifier_roberta/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- afb9ffb42b7348237a151d3dda3f0287aa20a2d2e2a02d831b4dc7f85fa72dd4
- Size of remote file:
- 3.58 kB
- SHA256:
- c5eb22648de3f6f50e00bdb0dcfa4b0ce5cb011a2933998bd7773a456f7de02e
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