Instructions to use Maunish/ext_sentbert-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maunish/ext_sentbert-5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Maunish/ext_sentbert-5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Maunish/ext_sentbert-5") model = AutoModel.from_pretrained("Maunish/ext_sentbert-5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Maunish/ext_sentbert-5: direct link, hf CLI and curl.
- Browser
- Download file 498 Bytes
-
https://huggingface.co/Maunish/ext_sentbert-5/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Maunish/ext_sentbert-5/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Maunish/ext_sentbert-5/resolve/main/tokenizer_config.json
498 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, "name_or_path": "../input/sentbert-downstream/outputs/0", "do_basic_tokenize": true, "never_split": null, "model_max_length": 512, "special_tokens_map_file": "/root/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-MiniLM-L6-v2/special_tokens_map.json", "tokenizer_class": "BertTokenizer"} |