Hugging Face model: bobox/DeBERTaV3-small-ST-AdaptiveLayer-Norm-ep2
Author: bobox
Created: 2024-06-12 15:38:28
Last modified: 2024-06-12 16:09:25
Pipeline: sentence-similarity
Library: sentence-transformers
Likes: 0 | Downloads: 44
Tags: sentence-transformers, pytorch, deberta-v2, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:67190, loss:AdaptiveLayerLoss, loss:MultipleNegativesRankingLoss, en, dataset:stanfordnlp/snli, arxiv:1908.10084, arxiv:2402.14776, arxiv:1705.00652, base_model:microsoft/deberta-v3-small, base_model:finetune:microsoft/deberta-v3-small, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base model relation: finetune
Base models: id: microsoft/deberta-v3-small
Card metadata:
language: en
library_name: sentence-transformers
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:67190, loss:AdaptiveLayerLoss, loss:MultipleNegativesRankingLoss
base_model: microsoft/deberta-v3-small
datasets: stanfordnlp/snli
metrics: cosine_accuracy, cosine_accuracy_threshold, cosine_f1, cosine_f1_threshold, cosine_precision, cosine_recall, cosine_ap, dot_accuracy, dot_accuracy_threshold, dot_f1, dot_f1_threshold, dot_precision, dot_recall, dot_ap, manhattan_accuracy, manhattan_accuracy_threshold, manhattan_f1, manhattan_f1_threshold, manhattan_precision, manhattan_recall, manhattan_ap, euclidean_accuracy, euclidean_accuracy_threshold, euclidean_f1, euclidean_f1_threshold, euclidean_precision, euclidean_recall, euclidean_ap, max_accuracy, max_accuracy_threshold, max_f1, max_f1_threshold, max_precision, max_recall, max_ap
pipeline_tag: sentence-similarity