Text Classification
Transformers
ONNX
Safetensors
Japanese
English
Chinese
GLiClass
gliclass
choice-classification
experimental
Instructions to use sugarknight/erabi-practical-v1-experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sugarknight/erabi-practical-v1-experimental with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sugarknight/erabi-practical-v1-experimental")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sugarknight/erabi-practical-v1-experimental", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from sugarknight/erabi-practical-v1-experimental: direct link, hf CLI and curl.
- Browser
- Download file 751 Bytes
-
https://huggingface.co/sugarknight/erabi-practical-v1-experimental/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://sugarknight/erabi-practical-v1-experimental/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/sugarknight/erabi-practical-v1-experimental/resolve/main/tokenizer_config.json
751 Bytes
| { | |
| "add_prefix_space": true, | |
| "backend": "tokenizers", | |
| "bos_token": "[CLS]", | |
| "clean_up_tokenization_spaces": false, | |
| "cls_token": "[CLS]", | |
| "do_lower_case": false, | |
| "eos_token": "[SEP]", | |
| "is_local": true, | |
| "local_files_only": true, | |
| "mask_token": "[MASK]", | |
| "max_length": 4096, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_to_multiple_of": null, | |
| "pad_token": "[PAD]", | |
| "pad_token_type_id": 0, | |
| "padding_side": "right", | |
| "sep_token": "[SEP]", | |
| "sp_model_kwargs": {}, | |
| "split_by_punct": false, | |
| "stride": 0, | |
| "tokenizer_class": "DebertaV2Tokenizer", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_id": 3, | |
| "unk_token": "[UNK]", | |
| "vocab_type": "spm" | |
| } | |