Instructions to use memorygreen/kobert_naver with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use memorygreen/kobert_naver with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="memorygreen/kobert_naver")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("memorygreen/kobert_naver") model = AutoModelForSequenceClassification.from_pretrained("memorygreen/kobert_naver", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 509 Bytes
6170167 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"auto_map": {
"AutoTokenizer": [
"tokenization_kobert.KoBertTokenizer",
"tokenization_kobert.KoBertTokenizer"
]
},
"backend": "tokenizers",
"cls_token": "[CLS]",
"do_lower_case": false,
"is_local": false,
"local_files_only": false,
"mask_token": "[MASK]",
"max_len": 512,
"model_max_length": 512,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
|