Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:2976
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Borsa356/bert_mnr_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Borsa356/bert_mnr_3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Borsa356/bert_mnr_3") sentences = [ "Two girls in red soccer uniforms running after a soccer ball.", "A man is holding a girls hand and walking through a creek.", "A boy is in a competition.", "Two girls are playing a game of soccer." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from Borsa356/bert_mnr_3: direct link, hf CLI and curl.
- Browser
- Download file 321 Bytes
-
https://huggingface.co/Borsa356/bert_mnr_3/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://Borsa356/bert_mnr_3/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/Borsa356/bert_mnr_3/resolve/main/1_Pooling/config.json
321 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 | |
| } |