Text Classification
Transformers
PyTorch
English
roberta
emotions
multi-class-classification
multi-label-classification
text-embeddings-inference
Instructions to use Rakshit122/roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rakshit122/roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Rakshit122/roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Rakshit122/roberta") model = AutoModelForSequenceClassification.from_pretrained("Rakshit122/roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Rakshit122/roberta: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Rakshit122/roberta/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Rakshit122/roberta/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Rakshit122/roberta/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 537343070ad3d888824e8f742d38728f75096db450b77d663395f3bb5a249ff7
- Size of remote file:
- 499 MB
- SHA256:
- 1e6198119f9a88968b60b1a3eecf7daf3cba4f75947ed5e92a6ed903b3cb2de0
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