Instructions to use memorygreen/kobart_naver with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use memorygreen/kobart_naver with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="memorygreen/kobart_naver")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("memorygreen/kobart_naver") model = AutoModelForSequenceClassification.from_pretrained("memorygreen/kobart_naver", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 297 Bytes
d06d77c | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"backend": "tokenizers",
"bos_token": "</s>",
"eos_token": "</s>",
"is_local": false,
"local_files_only": false,
"mask_token": "<mask>",
"model_max_length": 1000000000000000019884624838656,
"pad_token": "<pad>",
"tokenizer_class": "TokenizersBackend",
"unk_token": "<unk>"
}
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