Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:713598
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/FinetuningMiniLM-V23Data-256ConstantBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/FinetuningMiniLM-V23Data-256ConstantBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/FinetuningMiniLM-V23Data-256ConstantBATCH-SemanticEngine") sentences = [ "must kindergarten backpack mermazing 2 cases", "100 horse riding sleeveless gilet - black", " must backpack ", "bag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- eval/triplet_evaluation_results.csv +3 -0
- model.safetensors +1 -1
eval/triplet_evaluation_results.csv
CHANGED
|
@@ -1,3 +1,6 @@
|
|
| 1 |
epoch,steps,accuracy_cosine
|
| 2 |
0.3586800573888092,1000,0.9625617861747742
|
| 3 |
0.7173601147776184,2000,0.9603533744812012
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
epoch,steps,accuracy_cosine
|
| 2 |
0.3586800573888092,1000,0.9625617861747742
|
| 3 |
0.7173601147776184,2000,0.9603533744812012
|
| 4 |
+
1.0759856630824374,3000,0.9650856852531433
|
| 5 |
+
1.4344086021505376,4000,0.9645599126815796
|
| 6 |
+
1.792831541218638,5000,0.9647701978683472
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 133462128
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:dc7bd88319d45e161425d355a30be5576c14abf65566bfd3cd42936527d9a856
|
| 3 |
size 133462128
|