Instructions to use lots-o/ko-albert-large-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lots-o/ko-albert-large-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lots-o/ko-albert-large-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lots-o/ko-albert-large-v1") model = AutoModelForMaskedLM.from_pretrained("lots-o/ko-albert-large-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from lots-o/ko-albert-large-v1: direct link, hf CLI and curl.
- Browser
- Download file 367 Bytes
-
https://huggingface.co/lots-o/ko-albert-large-v1/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://lots-o/ko-albert-large-v1/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/lots-o/ko-albert-large-v1/resolve/main/tokenizer_config.json
367 Bytes
| { | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": false, | |
| "mask_token": "[MASK]", | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "strip_accents": false, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]", | |
| "model_max_length": 512 | |
| } |