Instructions to use phamson02/cotmae_biencoder2_170000_sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phamson02/cotmae_biencoder2_170000_sbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="phamson02/cotmae_biencoder2_170000_sbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("phamson02/cotmae_biencoder2_170000_sbert") model = AutoModelForMaskedLM.from_pretrained("phamson02/cotmae_biencoder2_170000_sbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from phamson02/cotmae_biencoder2_170000_sbert: direct link, hf CLI and curl.
- Browser
- Download file 284 Bytes
-
https://huggingface.co/phamson02/cotmae_biencoder2_170000_sbert/resolve/refs%2Fpr%2F1/1_Pooling/config.json
- Command line
-
hf download hf://phamson02/cotmae_biencoder2_170000_sbert@refs/pr/1/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/phamson02/cotmae_biencoder2_170000_sbert/resolve/refs%2Fpr%2F1/1_Pooling/config.json
284 Bytes
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false | |
| } |