Instructions to use acul3/bert-large-mc4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use acul3/bert-large-mc4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="acul3/bert-large-mc4")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("acul3/bert-large-mc4") model = AutoModelForMaskedLM.from_pretrained("acul3/bert-large-mc4") - Notebooks
- Google Colab
- Kaggle
| {"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, "special_tokens_map_file": "/home/samsulrahmadani/.cache/huggingface/transformers/7d5d7062e64a20f622a218bc2cce7a755714a6543f7f1b469388747d7ab8bfb0.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", "name_or_path": "indobenchmark/indobert-large-p2", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"} |