Model: slimaneMakh/triplet_CloseHlabel_farLabel_andnegativ-1M-5eps-XLMR_29may
Author: slimaneMakh
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, xlm-roberta, sentence-similarity, feature-extraction, dataset_size:100K<n<1M, loss:TripletLoss, arxiv:1908.10084, arxiv:1703.07737, base_model:FacebookAI/xlm-roberta-base, base_model:finetune:FacebookAI/xlm-roberta-base, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: FacebookAI/xlm-roberta-base
Card metadata: library_name: sentence-transformers
tags: sentence-transformers, sentence-similarity, feature-extraction, dataset_size:100K<n<1M, loss:TripletLoss
base_model: FacebookAI/xlm-roberta-base
metrics: cosine_accuracy, dot_accuracy, manhattan_accuracy, euclidean_accuracy, max_accuracy
widget: source_sentence: Skip
sentences: Ships, Kapital akcyjny, Other finance income, source_sentence: IIII
sentences: iii, Gauti dividendai, Loans given, source_sentence: IVE
sentences: HH, Koszty finansowe, Current borrowings, source_sentence: K K
sentences: TOTAL ACTIF, Nuomos mokejimai, Accruals, source_sentence: Sales
sentences: Revenue, Operating profit, Current borrowings
pipeline_tag: sentence-similarity
model-index: name: SentenceTransformer based on FacebookAI/xlm-roberta-base
results: task: type: triplet
name: Triplet
dataset: name: Unknown
type: unknown
metrics: type: cosine_accuracy
value: 0.9987885552019722
name: Cosine Accuracy
verified: no, type: dot_accuracy
value: 0.001529316610921369
name: Dot Accuracy
verified: no, type: manhattan_accuracy
value: 0.9975135360413657
name: Manhattan Accuracy
verified: no, type: euclidean_accuracy
value: 0.9990958312877694
name: Euclidean Accuracy
verified: no, type: max_accuracy
value: 0.9990958312877694
name: Max Accuracy
verified: no
Likes: 1 | All-time downloads: 157
Created: 2024-06-02 18:30:40