Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:647236
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/Finetunningv2MiniLM-V22Data-128ConstantBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/Finetunningv2MiniLM-V22Data-128ConstantBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/Finetunningv2MiniLM-V22Data-128ConstantBATCH-SemanticEngine") sentences = [ "essence multi task concealer 15 natural nude", "pure oxygen 20 vol", "essence", "face make-up" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| epoch,steps,accuracy_cosine | |
| 0.19774569903104608,1000,0.9700284004211426 | |
| 0.39549139806209216,2000,0.9702387452125549 | |
| 0.5932370970931382,3000,0.9673992991447449 | |
| 0.7909827961241843,4000,0.9680302739143372 | |
| 0.9887284951552304,5000,0.9672941565513611 | |
| 1.1864004744020558,6000,0.9699232578277588 | |
| 1.3840679976279897,7000,0.970974862575531 | |
| 1.5817355208539237,8000,0.9697129130363464 | |
| 1.7794030440798578,9000,0.9693974256515503 | |
| 1.9770705673057916,10000,0.9701335430145264 | |
| 2.1747380905317257,11000,0.9696077108383179 | |
| 2.3724056137576595,12000,0.9681354761123657 | |
| 2.5700731369835936,13000,0.9686612486839294 | |
| 2.767740660209528,14000,0.9686612486839294 | |
| 2.9654081834354615,15000,0.9696077108383179 | |