Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:100
loss:TripletLoss
text-embeddings-inference
Instructions to use DariaaaS/models-moved with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DariaaaS/models-moved with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DariaaaS/models-moved") sentences = [ "What is the CBSA name and type in York, ME?", "average household income refers to avg_income_per_household; city known as \"Danzig\" refers to bad_alias = 'Danzig';", "coordinates refers to latitude, longitude; latitude = '18.090875; longitude = '-66.867756'", "\"York\" is the city; 'ME' is the state; type refers to CBSA_type" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from DariaaaS/models-moved: direct link, hf CLI and curl.
- Browser
- Download file 296 Bytes
-
https://huggingface.co/DariaaaS/models-moved/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://DariaaaS/models-moved/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/DariaaaS/models-moved/resolve/main/1_Pooling/config.json
296 Bytes
| { | |
| "word_embedding_dimension": 384, | |
| "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, | |
| "include_prompt": true | |
| } |