Model: willy-arison/bge-base-financial-willy1
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: 0.23333333333333334
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.36666666666666664
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.4
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.6
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.23333333333333334
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.12222222222222222
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.08000000000000002
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.06000000000000002
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.23333333333333334
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.36666666666666664
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.4
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.6
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.39706836740181023
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.3353968253968253
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.3495891330891331
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: 0.3
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.36666666666666664
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.4
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: 0.12222222222222222
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.08000000000000002
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.06333333333333334
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.3
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.36666666666666664
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.4
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.6333333333333333
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.4352918015592833
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.37619047619047613
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.3885179603539039
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: 0.26666666666666666
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: 0.6
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: 0.06000000000000001
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: 0.6
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.40617236899479503
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.34707671957671954
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.35798156855024926
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: 0.3333333333333333
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.4
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.43333333333333335
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.5
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.3333333333333333
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.13333333333333333
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.08666666666666668
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.05000000000000001
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.3333333333333333
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.4
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.43333333333333335
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.5
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.40555269236416874
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.3767063492063492
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.39042863544785905
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: 0.36666666666666664
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.43333333333333335
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.4666666666666667
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.26666666666666666
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.12222222222222222
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.08666666666666668
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.046666666666666676
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.26666666666666666
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.36666666666666664
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.43333333333333335
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.4666666666666667
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.3579322900457958
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.32365079365079363
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.337741836612832
name: Cosine Map@100
verified: no
Likes: 0 | All-time downloads: 34
Created: 2025-06-26 09:01:37