Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:831141
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-v30-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-v30-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-v30-SemanticEngine") sentences = [ "gerber organic apple spinach with kale", "baby food", "flavor free baby food", "my beauty nail art set" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 2
Browse files- eval/triplet_evaluation_results.csv +3 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
CHANGED
|
@@ -5,3 +5,6 @@ epoch,steps,accuracy_cosine
|
|
| 5 |
1.231763619575254,4000,0.9738037586212158
|
| 6 |
1.5395506309633733,5000,0.9766557812690735
|
| 7 |
1.847337642351493,6000,0.9769726395606995
|
|
|
|
|
|
|
|
|
|
|
|
| 5 |
1.231763619575254,4000,0.9738037586212158
|
| 6 |
1.5395506309633733,5000,0.9766557812690735
|
| 7 |
1.847337642351493,6000,0.9769726395606995
|
| 8 |
+
2.155124653739612,7000,0.9763388633728027
|
| 9 |
+
2.4629116651277316,8000,0.9765501022338867
|
| 10 |
+
2.7706986765158508,9000,0.9763388633728027
|
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 sha256:64e3e97abef69613617c1d1a2c8b51ca62048872ed98822ecfb70063465aa429
|
| 3 |
size 90864192
|