Text Classification
Transformers
English
bert
bert-mini
classification
decision-model
dynamic-classification
calibrated-classification
option-scoring
oryn
Instructions to use timosarkar/oryn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use timosarkar/oryn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="timosarkar/oryn")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timosarkar/oryn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 471 Bytes
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"backend": "tokenizers",
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": true,
"is_local": false,
"local_files_only": false,
"mask_token": "[MASK]",
"model_max_length": 1000000000000000019884624838656,
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
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