Instructions to use CAUKiel/JavaBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CAUKiel/JavaBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="CAUKiel/JavaBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("CAUKiel/JavaBERT") model = AutoModelForMaskedLM.from_pretrained("CAUKiel/JavaBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from CAUKiel/JavaBERT: direct link, hf CLI and curl.
- Browser
- Download file 369 Bytes
-
https://huggingface.co/CAUKiel/JavaBERT/resolve/refs%2Fpr%2F2/tokenizer_config.json
- Command line
-
hf download hf://CAUKiel/JavaBERT@refs/pr/2/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/CAUKiel/JavaBERT/resolve/refs%2Fpr%2F2/tokenizer_config.json
369 Bytes
| {"do_lower_case": false, "do_basic_tokenize": true, "never_split": null, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "bert-base-cased", "tokenizer_class": "BertTokenizer"} | |