Model: willy-arison/bge-base-financial-willy3
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: 0.4
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.5
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.5666666666666667
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.6666666666666666
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.4
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.16666666666666663
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.11333333333333336
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.06666666666666668
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.4
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.5
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.5666666666666667
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.6666666666666666
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.520075424207518
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.4745238095238095
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.4911198244479979
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: 0.5
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.6333333333333333
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.7
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.36666666666666664
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.16666666666666663
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.12666666666666668
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.07000000000000002
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.36666666666666664
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.5
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.6333333333333333
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.7
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.5276816602931818
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.4730555555555555
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.48591804976272246
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: 0.43333333333333335
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.5666666666666667
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.5666666666666667
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.6666666666666666
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.43333333333333335
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.18888888888888886
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.11333333333333336
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.06666666666666668
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.43333333333333335
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.5666666666666667
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.5666666666666667
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.6666666666666666
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.5384601202448717
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.4984126984126984
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.5093647325589986
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: 0.3
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.5
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.6
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.6
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.3
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.16666666666666663
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.12000000000000002
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.06000000000000001
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.3
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.5
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.6
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.6
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.4445103975371312
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.3938888888888889
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.40394887488531556
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: 0.43333333333333335
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.5
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.5333333333333333
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.26666666666666666
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.14444444444444443
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.1
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.05333333333333334
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.26666666666666666
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.43333333333333335
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.5
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.5333333333333333
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.40728018212553163
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.36587301587301585
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.384350058982412
name: Cosine Map@100
verified: no
Likes: 0 | All-time downloads: 64
Created: 2025-06-26 09:41:21