Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:458830
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-V10Data-256BATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-V10Data-256BATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-V10Data-256BATCH-SemanticEngine") sentences = [ "derby cap toe shoes - brown", "chained strapped block heeled sandals", "100% premium natural leather - high quality sole.", "puppy treats biscuits" ] 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 +2 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
CHANGED
|
@@ -2,3 +2,5 @@ epoch,steps,accuracy_cosine
|
|
| 2 |
0.5577244841048522,1000,0.9485750198364258
|
| 3 |
1.1154489682097044,2000,0.9525712728500366
|
| 4 |
1.6731734523145567,3000,0.9576190710067749
|
|
|
|
|
|
|
|
|
| 2 |
0.5577244841048522,1000,0.9485750198364258
|
| 3 |
1.1154489682097044,2000,0.9525712728500366
|
| 4 |
1.6731734523145567,3000,0.9576190710067749
|
| 5 |
+
2.230897936419409,4000,0.957198441028595
|
| 6 |
+
2.788622420524261,5000,0.9595120549201965
|
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:f670f6b0c24a5989f204894338aca03767a54d03d2b764636fde2f5b76cadcce
|
| 3 |
size 90864192
|