Instructions to use halimara/model_sentence_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use halimara/model_sentence_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="halimara/model_sentence_bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("halimara/model_sentence_bert") model = AutoModel.from_pretrained("halimara/model_sentence_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from halimara/model_sentence_bert: direct link, hf CLI and curl.
- Browser
- Download file 527 Bytes
-
https://huggingface.co/halimara/model_sentence_bert/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://halimara/model_sentence_bert/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/halimara/model_sentence_bert/resolve/main/tokenizer_config.json
527 Bytes
| {"do_lower_case": true, "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "tokenize_chinese_chars": true, "strip_accents": null, "bos_token": "<s>", "eos_token": "</s>", "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "/root/.cache/torch/sentence_transformers/sbert.net_models_paraphrase-multilingual-MiniLM-L12-v2/0_Transformer"} |