Instructions to use skt/kobert-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skt/kobert-base-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="skt/kobert-base-v1")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("skt/kobert-base-v1") model = AutoModel.from_pretrained("skt/kobert-base-v1") - Inference
- Notebooks
- Google Colab
- Kaggle
update tokenizer
Browse files- special_tokens_map.json +1 -0
- spiece.model +0 -0
- tokenizer_config.json +1 -0
special_tokens_map.json
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{"unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": {"content": "[MASK]", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true}, "additional_special_tokens": ["<eop>", "<eod>"]}
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spiece.model
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tokenizer_config.json
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{"do_lower_case": false, "remove_space": true, "keep_accents": false, "bos_token": null, "eos_token": null, "unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": {"content": "[MASK]", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "additional_special_tokens": ["<eop>", "<eod>"], "sp_model_kwargs": {}, "tokenizer_class": "KoBERTTokenizer"}
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