Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Umesh/pulf-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Umesh/pulf-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Umesh/pulf-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Umesh/pulf-classifier") model = AutoModelForSequenceClassification.from_pretrained("Umesh/pulf-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Umesh/pulf-classifier: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/Umesh/pulf-classifier/resolve/main/training_args.bin
- Command line
-
hf download hf://Umesh/pulf-classifier/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Umesh/pulf-classifier/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 87071bd096e5633fd77596e74de23ab6fa6758908d33a4a8635a533ff87b17e0
- Size of remote file:
- 3.58 kB
- SHA256:
- bd10623042c771a0d4cd59664344b149fa1a43a0ca1917e8b4b6cd2ec11c6a48
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