Instructions to use anjandash/JavaBERT-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anjandash/JavaBERT-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anjandash/JavaBERT-small")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anjandash/JavaBERT-small") model = AutoModelForSequenceClassification.from_pretrained("anjandash/JavaBERT-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from anjandash/JavaBERT-small: direct link, hf CLI and curl.
- Browser
- Download file 364 Bytes
-
https://huggingface.co/anjandash/JavaBERT-small/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://anjandash/JavaBERT-small/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/anjandash/JavaBERT-small/resolve/main/tokenizer_config.json
364 Bytes
| {"do_lower_case": true, "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": "/home/akarmakar/Project2/tokenizer/jemma-java-bert-tokenizer", "tokenizer_class": "BertTokenizer"} |