Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:903776
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-v34-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-v34-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-v34-SemanticEngine") sentences = [ "pink n' yellow", "mirror", "pink mirror", "cups set of 3" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 3
Browse files- eval/triplet_evaluation_results.csv +4 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
CHANGED
|
@@ -9,3 +9,7 @@ epoch,steps,accuracy_cosine
|
|
| 9 |
2.2649306538352674,8000,0.9749630093574524
|
| 10 |
2.547976224172092,9000,0.9744347929954529
|
| 11 |
2.831021794508916,10000,0.9754912257194519
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 9 |
2.2649306538352674,8000,0.9749630093574524
|
| 10 |
2.547976224172092,9000,0.9744347929954529
|
| 11 |
2.831021794508916,10000,0.9754912257194519
|
| 12 |
+
3.11406736484574,11000,0.9754912257194519
|
| 13 |
+
3.397112935182564,12000,0.9757025241851807
|
| 14 |
+
3.6801585055193886,13000,0.9754912257194519
|
| 15 |
+
3.963204075856213,14000,0.9757025241851807
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 90864192
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7ae1820ad8749ba5506b9ad46441e3416199e61497438dea688fc5039bc63b56
|
| 3 |
size 90864192
|