Text Classification
Transformers
PyTorch
Safetensors
English
roberta
formality
text-embeddings-inference
Instructions to use s-nlp/roberta-base-formality-ranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use s-nlp/roberta-base-formality-ranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="s-nlp/roberta-base-formality-ranker")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("s-nlp/roberta-base-formality-ranker") model = AutoModelForSequenceClassification.from_pretrained("s-nlp/roberta-base-formality-ranker", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download model.safetensors from s-nlp/roberta-base-formality-ranker: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/s-nlp/roberta-base-formality-ranker/resolve/main/model.safetensors
- Command line
-
hf download hf://s-nlp/roberta-base-formality-ranker/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/s-nlp/roberta-base-formality-ranker/resolve/main/model.safetensors
499 MB
- Xet hash:
- 7979715af3de8b1e67f3cfcef758d7861cc6254d6d887f22aeb1ba17f50f6c55
- Size of remote file:
- 499 MB
- SHA256:
- e209ab54340ad202180d843fd1a76b62ac3da574c6f7efa9e2cba6a8db808199
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