Model: willy-arison/bge-base-financial-willy2
Author: willy-arison
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:264, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, en, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:BAAI/bge-base-en-v1.5, base_model:finetune:BAAI/bge-base-en-v1.5, license:apache-2.0, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: BAAI/bge-base-en-v1.5
Card metadata: language: en
license: apache-2.0
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:264, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget: source_sentence: OpenAI models
sentences: What is the name of the checkpoint obtained after fine-tuning and additional RL process?, Which models showed performance gains even after memorization performance had saturated?, What are the four techniques used for inducing knowledge acquisition in fine-tuned models?, source_sentence: Section 2.3 is about TRAINING.
sentences: What is the section 2.3 about?, In which publication and volume was the paper "Retrieval-augmented generation for knowledge-intensive nlp tasks" published?, Who are the authors of the paper "Removing rlhf protections in gpt-4 via fine-tuning"?, source_sentence: The main advantage of the mentioned approach over exact string matching is that it is significantly better at reducing false negatives (missing correct responses).
sentences: Who is the performance on the generalization task (Vignettes) compared to the Medical dataset questions?, Q:Where was the paper "Training language models to follow instructions with human feedback" published?, What is the main advantage of the mentioned approach over exact string matching?, source_sentence: Chunting Zhou, Pengfei Liu, Puxin Xu.
sentences: What type of questions are generated from each Python file in Scikit-Learn’s repository?, Who are the first three authors of the paper "Lima: Less is more for alignment"?, What model is used to generate QA pairs and rephrase them as short clinical vignettes?, source_sentence: The topic of the first paper is "Fine-tuning gpt-3 for russian text summarization."
sentences: What is the model's response when the date is changed to September 8, 2030?, What is the topic of the first paper mentioned in the text?, What is the prompt used in the masking example for the given fact statement?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics: cosine_accuracy@1, cosine_accuracy@3, cosine_accuracy@5, cosine_accuracy@10, cosine_precision@1, cosine_precision@3, cosine_precision@5, cosine_precision@10, cosine_recall@1, cosine_recall@3, cosine_recall@5, cosine_recall@10, cosine_ndcg@10, cosine_mrr@10, cosine_map@100
model-index: name: BGE base Financial Matryoshka
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 768
type: dim_768
metrics: type: cosine_accuracy@1
value: 0.3333333333333333
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.4666666666666667
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.5333333333333333
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.6
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.3333333333333333
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.15555555555555553
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.1066666666666667
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.06000000000000001
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.3333333333333333
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.4666666666666667
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.5333333333333333
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.6
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.460509769510084
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.41670634920634914
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.4369156706971833
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 512
type: dim_512
metrics: type: cosine_accuracy@1
value: 0.36666666666666664
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.4666666666666667
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.5
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.6666666666666666
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.36666666666666664
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.15555555555555553
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.10000000000000003
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.06666666666666668
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.36666666666666664
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.4666666666666667
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.5
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.6666666666666666
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.49505657348996124
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.44291005291005286
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.45921885667938295
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 256
type: dim_256
metrics: type: cosine_accuracy@1
value: 0.3
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.43333333333333335
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.5666666666666667
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.6333333333333333
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.3
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.14444444444444443
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.11333333333333336
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.06333333333333334
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.3
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.43333333333333335
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.5666666666666667
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.6333333333333333
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.4479220182693692
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.38972222222222225
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.4045775421572523
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 128
type: dim_128
metrics: type: cosine_accuracy@1
value: 0.26666666666666666
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.36666666666666664
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.4666666666666667
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.5666666666666667
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.26666666666666666
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.12222222222222222
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.09333333333333335
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.05666666666666667
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.26666666666666666
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.36666666666666664
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.4666666666666667
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.5666666666666667
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.396419334894427
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.3437037037037037
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.3563592849645481
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 64
type: dim_64
metrics: type: cosine_accuracy@1
value: 0.26666666666666666
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.3333333333333333
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.43333333333333335
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.5666666666666667
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.26666666666666666
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.11111111111111112
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.08666666666666668
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.05666666666666667
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.26666666666666666
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.3333333333333333
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.43333333333333335
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.5666666666666667
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.3868834679921395
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.33240740740740743
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.34576834243694327
name: Cosine Map@100
verified: no
Likes: 0 | All-time downloads: 43
Created: 2025-06-26 09:16:29