Instructions to use raygx/GNePT-NepSA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raygx/GNePT-NepSA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="raygx/GNePT-NepSA")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("raygx/GNePT-NepSA") model = AutoModelForSequenceClassification.from_pretrained("raygx/GNePT-NepSA") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer
Browse files- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +6 -0
special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tokenizer.json
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tokenizer_config.json
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{
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"clean_up_tokenization_spaces": true,
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"model_max_length": 512,
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"padding_side": "left",
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"tokenizer_class": "PreTrainedTokenizerFast"
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}
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