Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:798551
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-v28-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-v28-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-v28-SemanticEngine") sentences = [ "chillax fluffy beanbag", "living room furniture", "lined orange", "home_garden", "home and garden", "beanbag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [6, 6] - 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 |
1.2818705957719412,4000,0.9779156446456909
|
| 6 |
1.6021780909673287,5000,0.9798086285591125
|
| 7 |
1.9224855861627161,6000,0.9800189137458801
|
|
|
|
|
|
|
|
|
|
|
|
| 5 |
1.2818705957719412,4000,0.9779156446456909
|
| 6 |
1.6021780909673287,5000,0.9798086285591125
|
| 7 |
1.9224855861627161,6000,0.9800189137458801
|
| 8 |
+
2.2427930813581036,7000,0.9794931411743164
|
| 9 |
+
2.5631005765534915,8000,0.9803344011306763
|
| 10 |
+
2.883408071748879,9000,0.9803344011306763
|
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 sha256:1c3a0ac20d79909313f24cf7314a29193956dd93d7cc76cdf06710a9ff1fa2b8
|
| 3 |
size 90864192
|