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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13b07d265dcfbf958b2fda9052684855a1f452059bd7f0ba3d1429137fbce2c0
- Size of remote file:
- 499 MB
- SHA256:
- 796e2f97cfb1e650aad8abc564d8217ada897d39226929ccddcec734f7ebf370
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.