Instructions to use Pra-tham/roberta-binary-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pra-tham/roberta-binary-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Pra-tham/roberta-binary-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Pra-tham/roberta-binary-classifier") model = AutoModelForSequenceClassification.from_pretrained("Pra-tham/roberta-binary-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from Pra-tham/roberta-binary-classifier: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Pra-tham/roberta-binary-classifier/resolve/main/model.safetensors
- Command line
-
hf download hf://Pra-tham/roberta-binary-classifier/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Pra-tham/roberta-binary-classifier/resolve/main/model.safetensors
499 MB
- Xet hash:
- 13b07d265dcfbf958b2fda9052684855a1f452059bd7f0ba3d1429137fbce2c0
- Size of remote file:
- 499 MB
- SHA256:
- 796e2f97cfb1e650aad8abc564d8217ada897d39226929ccddcec734f7ebf370
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