Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:12800
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Borsa356/bert_mnr_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Borsa356/bert_mnr_6 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Borsa356/bert_mnr_6") sentences = [ "The dog wades through deep snow with something in its mouth.", "The dog is outside during winter.", "A man is wearing a helmet.", "There are dogs racing at a track." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from Borsa356/bert_mnr_6: direct link, hf CLI and curl.
- Browser
- Download file 242 Bytes
-
https://huggingface.co/Borsa356/bert_mnr_6/resolve/main/modules.json
- Command line
-
hf download hf://Borsa356/bert_mnr_6/modules.json
-
curl -L -o modules.json https://huggingface.co/Borsa356/bert_mnr_6/resolve/main/modules.json
242 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |