Instructions to use jinbbong/kbert_base_esg_e5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinbbong/kbert_base_esg_e5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jinbbong/kbert_base_esg_e5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jinbbong/kbert_base_esg_e5") model = AutoModelForMaskedLM.from_pretrained("jinbbong/kbert_base_esg_e5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from jinbbong/kbert_base_esg_e5: direct link, hf CLI and curl.
- Browser
- Download file 372 Bytes
-
https://huggingface.co/jinbbong/kbert_base_esg_e5/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://jinbbong/kbert_base_esg_e5/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/jinbbong/kbert_base_esg_e5/resolve/main/tokenizer_config.json
372 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": false, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "kykim/bert-kor-base", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"} |