Text Classification
Transformers
PyTorch
Safetensors
English
roberta
formality
text-embeddings-inference
Instructions to use cointegrated/roberta-base-formality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cointegrated/roberta-base-formality with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cointegrated/roberta-base-formality")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cointegrated/roberta-base-formality") model = AutoModelForSequenceClassification.from_pretrained("cointegrated/roberta-base-formality", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cointegrated/roberta-base-formality: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/cointegrated/roberta-base-formality/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://cointegrated/roberta-base-formality@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cointegrated/roberta-base-formality/resolve/refs%2Fpr%2F1/pytorch_model.bin
499 MB
- Xet hash:
- 5e81ffb44e81da842fd3a8b397abbc363779b43c134db21038a6ad2e55feb3cd
- Size of remote file:
- 499 MB
- SHA256:
- 143907f57340e6583a99585635e8c63087d6d0e3eb0b75941b937ad397f73f13
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