Instructions to use Maunish/ecomm-sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maunish/ecomm-sbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Maunish/ecomm-sbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Maunish/ecomm-sbert") model = AutoModelForMaskedLM.from_pretrained("Maunish/ecomm-sbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Maunish/ecomm-sbert: direct link, hf CLI and curl.
- Browser
- Download file 562 Bytes
-
https://huggingface.co/Maunish/ecomm-sbert/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Maunish/ecomm-sbert/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Maunish/ecomm-sbert/resolve/main/tokenizer_config.json
562 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": "sentence-transformers/paraphrase-MiniLM-L6-v2", "do_basic_tokenize": true, "never_split": null, "model_max_length": 512, "special_tokens_map_file": "/root/.cache/huggingface/transformers/d574f6367feebd1fb66c3cb1c9a098a1a311269a7de26d554dd3fc9a7c95ec8d.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", "tokenizer_class": "BertTokenizer"} |