Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:291522
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/MiniLM-256BATCH-V6Data-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/MiniLM-256BATCH-V6Data-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/MiniLM-256BATCH-V6Data-SemanticEngine") sentences = [ "cream 21 baby oil with almond oil", "hi, barbie! bundle", "nourishing baby oil", "material: wooden. size: 15 x 30 cm." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1, checkpoint
Browse files
last-checkpoint/trainer_state.json
CHANGED
|
@@ -27,9 +27,9 @@
|
|
| 27 |
"epoch": 1.0,
|
| 28 |
"eval_cosine_accuracy": 0.9113098382949829,
|
| 29 |
"eval_loss": 0.8482398986816406,
|
| 30 |
-
"eval_runtime":
|
| 31 |
-
"eval_samples_per_second":
|
| 32 |
-
"eval_steps_per_second": 1.
|
| 33 |
"step": 1139
|
| 34 |
}
|
| 35 |
],
|
|
|
|
| 27 |
"epoch": 1.0,
|
| 28 |
"eval_cosine_accuracy": 0.9113098382949829,
|
| 29 |
"eval_loss": 0.8482398986816406,
|
| 30 |
+
"eval_runtime": 34.6129,
|
| 31 |
+
"eval_samples_per_second": 274.608,
|
| 32 |
+
"eval_steps_per_second": 1.098,
|
| 33 |
"step": 1139
|
| 34 |
}
|
| 35 |
],
|
last-checkpoint/training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 5752
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0b003a2d7d735bc23e787cd4f733f53209d08e4cdf3b8af1d2531e29e0efe3b3
|
| 3 |
size 5752
|