Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dataset_size:1M<n<10M
loss:TripletLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use gubartz/facet_retriever with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gubartz/facet_retriever with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gubartz/facet_retriever") sentences = [ "method: Making reflective work practices visible", "method: Job quality takes into account both wage and non-wage attributes of a job.", "purpose: There could therefore be rank differences in the leadership behavioural patterns of managers.", "negative: SN has a positive effect on the user's intention to use toward the SNS." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from gubartz/facet_retriever: direct link, hf CLI and curl.
- Browser
- Download file 296 Bytes
-
https://huggingface.co/gubartz/facet_retriever/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://gubartz/facet_retriever/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/gubartz/facet_retriever/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 | |
| } |