Instructions to use kazma1/simcse-robertaLarge-matching with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kazma1/simcse-robertaLarge-matching with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, RobertaForCL tokenizer = AutoTokenizer.from_pretrained("kazma1/simcse-robertaLarge-matching") model = RobertaForCL.from_pretrained("kazma1/simcse-robertaLarge-matching", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 622 Bytes
9b60c18 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"bos_token": "[CLS]",
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": false,
"eos_token": "[SEP]",
"mask_token": "[MASK]",
"model_max_length": 512,
"name_or_path": "output/simcse-robertLarge/best_model",
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"special_tokens_map_file": "C:\\Users\\Administrator/.cache\\huggingface\\hub\\models--klue--roberta-small\\snapshots\\5fe1f0cb3946f0ea1c01e657cd1688771cf47802\\special_tokens_map.json",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
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