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 tokenizer_config.json from cointegrated/roberta-base-formality: direct link, hf CLI and curl.
- Browser
- Download file 288 Bytes
-
https://huggingface.co/cointegrated/roberta-base-formality/resolve/refs%2Fpr%2F1/tokenizer_config.json
- Command line
-
hf download hf://cointegrated/roberta-base-formality@refs/pr/1/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/cointegrated/roberta-base-formality/resolve/refs%2Fpr%2F1/tokenizer_config.json
288 Bytes
| {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "add_prefix_space": false, "errors": "replace", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>", "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "roberta-base"} |