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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e81ffb44e81da842fd3a8b397abbc363779b43c134db21038a6ad2e55feb3cd
- Size of remote file:
- 499 MB
- SHA256:
- 143907f57340e6583a99585635e8c63087d6d0e3eb0b75941b937ad397f73f13
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.