Model: willy-arison/bge-base-financial-willy
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: 0.26666666666666666
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.3333333333333333
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.36666666666666664
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: 0.07333333333333333
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.05666666666666668
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: 0.36666666666666664
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.5666666666666667
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.3838336301118898
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.3309259259259259
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.34343436271505323
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: 0.26666666666666666
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.3333333333333333
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.36666666666666664
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.5
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: 0.07333333333333333
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.05000000000000002
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: 0.36666666666666664
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.5
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.36661904860357913
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.32652116402116405
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.3453772498337715
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: 0.23333333333333334
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.3333333333333333
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.36666666666666664
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.5333333333333333
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.23333333333333334
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.11111111111111112
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.07333333333333333
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.053333333333333344
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.23333333333333334
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.3333333333333333
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.36666666666666664
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.5333333333333333
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.35800954456310863
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.3059259259259258
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.31868615950830104
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: 0.2
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.36666666666666664
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.43333333333333335
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.5333333333333333
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.2
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.12222222222222222
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.08666666666666668
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.053333333333333344
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.2
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.36666666666666664
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.43333333333333335
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.5333333333333333
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.3480374940126532
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.2912037037037037
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.2984413665207766
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: 0.4
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.4666666666666667
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: 0.08000000000000002
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.046666666666666676
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: 0.4
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.4666666666666667
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.3487600459577948
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.31277777777777777
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.3249634443232781
name: Cosine Map@100
verified: no
Likes: 0 | All-time downloads: 33
Created: 2025-06-26 08:54:33