base_model: Alibaba-NLP/gte-large-en-v1.5 datasets: [] language:

• en library_name: sentence-transformers license: apache-2.0 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 pipeline_tag: sentence-similarity tags:
• sentence-transformers
• sentence-similarity
• feature-extraction
• generated_from_trainer
• dataset_size:4275
• loss:MatryoshkaLoss
• loss:MultipleNegativesRankingLoss widget:
• source_sentence: The fundamental elements of Goldman Sachs’ robust risk culture include governance, risk identification, measurement, mitigation, culture and conduct, and infrastructure. They believe these elements work together to complement and reinforce each other to produce a comprehensive view of risk management. sentences:
  • What are the financial highlights for Bank of America Corp. in its latest fiscal year report?
  • What is Berkshire Hathaway's involvement in the energy sector?
  • What is Goldman Sach’s approach towards maintaining a robust risk culture?
• source_sentence: HealthTech Inc.'s new drug for diabetes treatment, launched in 2021, contributed to approximately 30% of its total revenues for that year. sentences:
  • What is IBM's debt to equity ratio as of 2022?
  • In what way does HealthTech Inc's new drug contribute to its revenue generation?
  • What is the revenue breakdown of Alphabet for the year 2021?
• source_sentence: The driving factor behind Tesla’s 2023 growth was the surge in demand for electric vehicles. sentences:
  • Why did McDonald's observe a decrease in overall revenue in 2023 relative to 2022?
  • What key strategy did Walmart employ to boost its sales in 2016?
  • What was the driving factor behind Tesla's growth in 2023?
• source_sentence: Pfizer is committed to ensuring that people around the world have access to its medical products. In line with this commitment, Pfizer has implemented programs such as donation drives, price reduction initiatives, and patient assistance programs to aid those in need. Furthermore, through partnerships with NGOs and governments, Pfizer strives to strengthen healthcare systems in underprivileged regions. sentences:
  • What is the strategy of Pfizer to improve access to medicines in underprivileged areas?
  • What percentage of growth in revenue did Adobe Systems report in June 2020?
  • How is Citigroup differentiating itself among other banks?
• source_sentence: JP Morgan reported total deposits of $2.6 trillion in the year ending December 31, 2023. sentences:
  • In the fiscal year 2023, what impact did the acquisition of T-Mobile bring to the revenue of AT&T?
  • What is the primary source of revenue for the software company, Microsoft?
  • What were JP Morgan's total deposits in 2023? model-index:
• name: gte-large-en-v1.5-financial-rag-matryoshka results:
  • task: type: information-retrieval name: Information Retrieval dataset: name: dim 1024 type: dim_1024 metrics:
    • type: cosine_accuracy@1 value: 0.88 name: Cosine Accuracy@1
    • type: cosine_accuracy@3 value: 0.96 name: Cosine Accuracy@3
    • type: cosine_accuracy@5 value: 0.9866666666666667 name: Cosine Accuracy@5
    • type: cosine_accuracy@10 value: 0.9955555555555555 name: Cosine Accuracy@10
    • type: cosine_precision@1 value: 0.88 name: Cosine Precision@1
    • type: cosine_precision@3 value: 0.32 name: Cosine Precision@3
    • type: cosine_precision@5 value: 0.19733333333333336 name: Cosine Precision@5
    • type: cosine_precision@10 value: 0.09955555555555556 name: Cosine Precision@10
    • type: cosine_recall@1 value: 0.88 name: Cosine Recall@1
    • type: cosine_recall@3 value: 0.96 name: Cosine Recall@3
    • type: cosine_recall@5 value: 0.9866666666666667 name: Cosine Recall@5
    • type: cosine_recall@10 value: 0.9955555555555555 name: Cosine Recall@10
    • type: cosine_ndcg@10 value: 0.9426916896167131 name: Cosine Ndcg@10
    • type: cosine_mrr@10 value: 0.9251851851851851 name: Cosine Mrr@10
    • type: cosine_map@100 value: 0.925362962962963 name: Cosine Map@100
  • task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics:
    • type: cosine_accuracy@1 value: 0.88 name: Cosine Accuracy@1
    • type: cosine_accuracy@3 value: 0.96 name: Cosine Accuracy@3
    • type: cosine_accuracy@5 value: 0.9866666666666667 name: Cosine Accuracy@5
    • type: cosine_accuracy@10 value: 0.9911111111111112 name: Cosine Accuracy@10
    • type: cosine_precision@1 value: 0.88 name: Cosine Precision@1
    • type: cosine_precision@3 value: 0.32 name: Cosine Precision@3
    • type: cosine_precision@5 value: 0.19733333333333336 name: Cosine Precision@5
    • type: cosine_precision@10 value: 0.09911111111111114 name: Cosine Precision@10
    • type: cosine_recall@1 value: 0.88 name: Cosine Recall@1
    • type: cosine_recall@3 value: 0.96 name: Cosine Recall@3
    • type: cosine_recall@5 value: 0.9866666666666667 name: Cosine Recall@5
    • type: cosine_recall@10 value: 0.9911111111111112 name: Cosine Recall@10
    • type: cosine_ndcg@10 value: 0.940825047039427 name: Cosine Ndcg@10
    • type: cosine_mrr@10 value: 0.924 name: Cosine Mrr@10
    • type: cosine_map@100 value: 0.9245274971941638 name: Cosine Map@100
  • task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics:
    • type: cosine_accuracy@1 value: 0.8711111111111111 name: Cosine Accuracy@1
    • type: cosine_accuracy@3 value: 0.96 name: Cosine Accuracy@3
    • type: cosine_accuracy@5 value: 0.9866666666666667 name: Cosine Accuracy@5
    • type: cosine_accuracy@10 value: 0.9911111111111112 name: Cosine Accuracy@10
    • type: cosine_precision@1 value: 0.8711111111111111 name: Cosine Precision@1
    • type: cosine_precision@3 value: 0.32 name: Cosine Precision@3
    • type: cosine_precision@5 value: 0.19733333333333336 name: Cosine Precision@5
    • type: cosine_precision@10 value: 0.09911111111111114 name: Cosine Precision@10
    • type: cosine_recall@1 value: 0.8711111111111111 name: Cosine Recall@1
    • type: cosine_recall@3 value: 0.96 name: Cosine Recall@3
    • type: cosine_recall@5 value: 0.9866666666666667 name: Cosine Recall@5
    • type: cosine_recall@10 value: 0.9911111111111112 name: Cosine Recall@10
    • type: cosine_ndcg@10 value: 0.938126332642602 name: Cosine Ndcg@10
    • type: cosine_mrr@10 value: 0.9202962962962962 name: Cosine Mrr@10
    • type: cosine_map@100 value: 0.9207248677248678 name: Cosine Map@100
  • task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics:
    • type: cosine_accuracy@1 value: 0.8755555555555555 name: Cosine Accuracy@1
    • type: cosine_accuracy@3 value: 0.96 name: Cosine Accuracy@3
    • type: cosine_accuracy@5 value: 0.9866666666666667 name: Cosine Accuracy@5
    • type: cosine_accuracy@10 value: 0.9911111111111112 name: Cosine Accuracy@10
    • type: cosine_precision@1 value: 0.8755555555555555 name: Cosine Precision@1
    • type: cosine_precision@3 value: 0.32 name: Cosine Precision@3
    • type: cosine_precision@5 value: 0.19733333333333336 name: Cosine Precision@5
    • type: cosine_precision@10 value: 0.09911111111111114 name: Cosine Precision@10
    • type: cosine_recall@1 value: 0.8755555555555555 name: Cosine Recall@1
    • type: cosine_recall@3 value: 0.96 name: Cosine Recall@3
    • type: cosine_recall@5 value: 0.9866666666666667 name: Cosine Recall@5
    • type: cosine_recall@10 value: 0.9911111111111112 name: Cosine Recall@10
    • type: cosine_ndcg@10 value: 0.9395718726230007 name: Cosine Ndcg@10
    • type: cosine_mrr@10 value: 0.9222962962962963 name: Cosine Mrr@10
    • type: cosine_map@100 value: 0.9227724867724867 name: Cosine Map@100
  • task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics:
    • type: cosine_accuracy@1 value: 0.8666666666666667 name: Cosine Accuracy@1
    • type: cosine_accuracy@3 value: 0.9555555555555556 name: Cosine Accuracy@3
    • type: cosine_accuracy@5 value: 0.9866666666666667 name: Cosine Accuracy@5
    • type: cosine_accuracy@10 value: 0.9911111111111112 name: Cosine Accuracy@10
    • type: cosine_precision@1 value: 0.8666666666666667 name: Cosine Precision@1
    • type: cosine_precision@3 value: 0.3185185185185185 name: Cosine Precision@3
    • type: cosine_precision@5 value: 0.19733333333333336 name: Cosine Precision@5
    • type: cosine_precision@10 value: 0.09911111111111114 name: Cosine Precision@10
    • type: cosine_recall@1 value: 0.8666666666666667 name: Cosine Recall@1
    • type: cosine_recall@3 value: 0.9555555555555556 name: Cosine Recall@3
    • type: cosine_recall@5 value: 0.9866666666666667 name: Cosine Recall@5
    • type: cosine_recall@10 value: 0.9911111111111112 name: Cosine Recall@10
    • type: cosine_ndcg@10 value: 0.9346269584282435 name: Cosine Ndcg@10
    • type: cosine_mrr@10 value: 0.9157037037037037 name: Cosine Mrr@10
    • type: cosine_map@100 value: 0.9160403095943067 name: Cosine Map@100
  • task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics:
    • type: cosine_accuracy@1 value: 0.8311111111111111 name: Cosine Accuracy@1
    • type: cosine_accuracy@3 value: 0.96 name: Cosine Accuracy@3
    • type: cosine_accuracy@5 value: 0.9733333333333334 name: Cosine Accuracy@5
    • type: cosine_accuracy@10 value: 0.9911111111111112 name: Cosine Accuracy@10
    • type: cosine_precision@1 value: 0.8311111111111111 name: Cosine Precision@1
    • type: cosine_precision@3 value: 0.32 name: Cosine Precision@3
    • type: cosine_precision@5 value: 0.19466666666666665 name: Cosine Precision@5
    • type: cosine_precision@10 value: 0.09911111111111114 name: Cosine Precision@10
    • type: cosine_recall@1 value: 0.8311111111111111 name: Cosine Recall@1
    • type: cosine_recall@3 value: 0.96 name: Cosine Recall@3
    • type: cosine_recall@5 value: 0.9733333333333334 name: Cosine Recall@5
    • type: cosine_recall@10 value: 0.9911111111111112 name: Cosine Recall@10
    • type: cosine_ndcg@10 value: 0.9208110890988729 name: Cosine Ndcg@10
    • type: cosine_mrr@10 value: 0.8971957671957672 name: Cosine Mrr@10
    • type: cosine_map@100 value: 0.8975242479721762 name: Cosine Map@100

financial-rag-matryoshka

Model finetuned for financial use-cases from Alibaba-NLP/gte-large-en-v1.5. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

This model strives to excel tremendously in Financial Document Retrieval Tasks, concurrently preserving a maximum level of generalized performance.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: Alibaba-NLP/gte-large-en-v1.5
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 1024 tokens
• Similarity Function: Cosine Similarity

• Language: en
• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("rbhatia46/gte-large-en-v1.5-financial-rag-matryoshka")
# Run inference
sentences = [
    'JP Morgan reported total deposits of $2.6 trillion in the year ending December 31, 2023.',
    "What were JP Morgan's total deposits in 2023?",
    'What is the primary source of revenue for the software company, Microsoft?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

• Dataset: dim_1024
• Evaluated with InformationRetrievalEvaluator

Information Retrieval

• Dataset: dim_768
• Evaluated with InformationRetrievalEvaluator

Information Retrieval

• Dataset: dim_512
• Evaluated with InformationRetrievalEvaluator

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator

Information Retrieval

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator

Information Retrieval

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator

Training Details

Training Dataset

Unnamed Dataset

• Size: 4,275 training samples
• Columns: positive and anchor
• Approximate statistics based on the first 1000 samples:

  	positive	anchor
  type	string	string
  details	min: 15 tokens mean: 44.74 tokens max: 114 tokens	min: 9 tokens mean: 18.12 tokens max: 32 tokens

• Samples:

  positive	anchor
  At the end of fiscal year 2023, Exxon Mobil reported a debt-to-equity ratio of 0.32, implying that the company used more equity than debt in its capital structure.	What was the debt-to-equity ratio for Exxon Mobil at the end of fiscal year 2023?
  Amazon Web Services (AWS) generated $12.7 billion in net sales in the fourth quarter of 2020, up 28% from the same period of the previous year. It accounted for about 10% of Amazon’s total net sales for the quarter.	How did Amazon's AWS segment perform in the fourth quarter of 2020?
  JPMorgan Chase generates revenues by providing a wide range of banking and financial services. These include investment banking (M&As, advisory), consumer and community banking (home mortgages, auto loans), commercial banking, and asset and wealth management.	What are the key revenue sources for JPMorgan Chase?

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 16
• gradient_accumulation_steps: 16
• learning_rate: 2e-05
• num_train_epochs: 10
• lr_scheduler_type: cosine
• warmup_ratio: 0.1
• bf16: True
• tf32: True
• load_best_model_at_end: True
• optim: adamw_torch_fused
• batch_sampler: no_duplicates

All Hyperparameters

Training Logs

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.6
• Sentence Transformers: 3.0.1
• Transformers: 4.41.2
• PyTorch: 2.1.2+cu121
• Accelerate: 0.32.1
• Datasets: 2.19.1
• Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}