Text Classification
Transformers
PyTorch
Safetensors
English
roberta
argument-mining
opinion-mining
information-extraction
inference-extraction
Twitter
Instructions to use TomatenMarc/WRAP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TomatenMarc/WRAP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TomatenMarc/WRAP")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TomatenMarc/WRAP") model = AutoModelForSequenceClassification.from_pretrained("TomatenMarc/WRAP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from TomatenMarc/WRAP: direct link, hf CLI and curl.
- Browser
- Download file 328 Bytes
-
https://huggingface.co/TomatenMarc/WRAP/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://TomatenMarc/WRAP/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/TomatenMarc/WRAP/resolve/main/tokenizer_config.json
328 Bytes
| { | |
| "bos_token": "<s>", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "<s>", | |
| "do_lower_case": false, | |
| "eos_token": "</s>", | |
| "mask_token": "<mask>", | |
| "model_max_length": 128, | |
| "normalization": false, | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "tokenizer_class": "BertweetTokenizer", | |
| "unk_token": "<unk>" | |
| } | |