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
File size: 751 Bytes
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"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"
}
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