Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use mahwizzzz/RomanUrduClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mahwizzzz/RomanUrduClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mahwizzzz/RomanUrduClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mahwizzzz/RomanUrduClassification") model = AutoModelForSequenceClassification.from_pretrained("mahwizzzz/RomanUrduClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mahwizzzz/RomanUrduClassification: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/mahwizzzz/RomanUrduClassification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mahwizzzz/RomanUrduClassification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mahwizzzz/RomanUrduClassification/resolve/main/pytorch_model.bin
504 MB
- Xet hash:
- 58907acb1da86b6162b4d9eab09f9083a34fbf77d7c6259e222d47729e8ec361
- Size of remote file:
- 504 MB
- SHA256:
- 380ede3b10404f44416c0f00f5ec04328560b28b7a7053b35385dd88360435c3
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