Sentence Similarity
sentence-transformers
Safetensors
new
feature-extraction
Generated from Trainer
dataset_size:43
loss:MultipleNegativesRankingLoss
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use gaggi009/sbert-su-docs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gaggi009/sbert-su-docs with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gaggi009/sbert-su-docs", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download eval/triplet_evaluation_databricks_data_results.csv from gaggi009/sbert-su-docs: direct link, hf CLI and curl.
- Browser
- Download file 219 Bytes
-
https://huggingface.co/gaggi009/sbert-su-docs/resolve/main/eval/triplet_evaluation_databricks_data_results.csv
- Command line
-
hf download hf://gaggi009/sbert-su-docs/eval/triplet_evaluation_databricks_data_results.csv
-
curl -L -o triplet_evaluation_databricks_data_results.csv https://huggingface.co/gaggi009/sbert-su-docs/resolve/main/eval/triplet_evaluation_databricks_data_results.csv
219 Bytes
| epoch,steps,accuracy_cosine | |
| 1.0,11,0.20000000298023224 | |
| 2.0,22,0.4000000059604645 | |
| 3.0,33,0.4000000059604645 | |
| 4.0,44,0.4000000059604645 | |
| 5.0,55,0.4000000059604645 | |
| 6.0,66,0.4000000059604645 | |
| 7.0,77,0.4000000059604645 | |