Sentence Similarity
sentence-transformers
Safetensors
Slovak
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:137745
loss:CosineSimilarityLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use borsimnet/e5-sk-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use borsimnet/e5-sk-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("borsimnet/e5-sk-large") sentences = [ "Mor a epidémia sa očividne vymkli spod kontroly .", "Choroba bola nekontrolovateľná a ohrozovala všetok život .", "Tieto vylúčenia sú určené na iné cieľové skupiny ako obchodné štvrte .", "Autobus National Trust Tour odchádza každý deň o 9:00 z National Trust Information Centre ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 518 Bytes
7d86055 | 1 2 3 4 5 | epoch,steps,cosine_accuracy,cosine_accuracy_threshold,cosine_f1,cosine_precision,cosine_recall,cosine_f1_threshold,cosine_ap,cosine_mcc
1.0,60,0.6662650602409639,0.4996986136226642,0.33319935691318325,0.9987951807228915,0.27417349840077787,-0.010024086747057818
2.0,120,0.6662650602409639,0.4995480566435673,0.33306548814785053,0.9987951807228915,0.2742709130439137,-0.028346702743853168
3.0,180,0.6662650602409639,0.4995480566435673,0.33306548814785053,0.9987951807228915,0.2727233647368423,-0.028346702743853168
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