Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:705905
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/v2MiniLM-V18Data-256ConstantBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/v2MiniLM-V18Data-256ConstantBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/v2MiniLM-V18Data-256ConstantBATCH-SemanticEngine") sentences = [ "gerber baby food fruits apples bananas & cereal", "world of sweets puzzle", "baby food", "baby food" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 5
Browse files- eval/triplet_evaluation_results.csv +2 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
CHANGED
|
@@ -10,3 +10,5 @@ epoch,steps,accuracy_cosine
|
|
| 10 |
3.2632342277012327,9000,0.9682406187057495
|
| 11 |
3.625815808556925,10000,0.9673992991447449
|
| 12 |
3.9883973894126177,11000,0.9668734669685364
|
|
|
|
|
|
|
|
|
| 10 |
3.2632342277012327,9000,0.9682406187057495
|
| 11 |
3.625815808556925,10000,0.9673992991447449
|
| 12 |
3.9883973894126177,11000,0.9668734669685364
|
| 13 |
+
4.350978970268311,12000,0.9690819382667542
|
| 14 |
+
4.713560551124003,13000,0.9675044417381287
|
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 sha256:9f80e14052eeb507188e23c3a59dfb9a00d1d0a2e8ef2c92fa229ecdfa918c2d
|
| 3 |
size 90864192
|