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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:6300 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: nomic-ai/nomic-embed-text-v1 |
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widget: |
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- source_sentence: What amount of senior notes was repaid during fiscal 2022? |
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sentences: |
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- 'The following table sets forth the breakdown of revenue by geography, determined |
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based on the location of the Host’s listing (in millions): | Year Ended December |
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31, | 2021 | 2022 | 2023 United States | $ | 2,996 | | $ | 3,890 | $ | 4,290 International(1) |
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| 2,996 | | 4,509 | | 5,627 Total revenue | $ | 5,992 | | $ | 8,399 | $ | 9,917' |
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- During fiscal 2022, $2.25 billion of senior notes was repaid. |
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- Several factors are considered in developing the estimate for the long-term expected |
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rate of return on plan assets. For the defined benefit retirement plans, these |
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factors include historical rates of return of broad equity and bond indices and |
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projected long-term rates of return obtained from pension investment consultants. |
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The expected long-term rates of return for plan assets are 8 - 9% for equities |
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and 3 - 5% for bonds. For other retiree benefit plans, the expected long-term |
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rate of return reflects that the assets are comprised primarily of Company stock. |
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The expected rate of return on Company stock is based on the long-term projected |
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return of 8.5% and reflects the historical pattern of returns. |
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- source_sentence: What does GameStop Corp. offer to its customers? |
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sentences: |
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- State fraud and abuse laws could lead to criminal, civil, or administrative consequences, |
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including licensure loss, exclusion from healthcare programs, and significant |
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negative effects on the violating entity's business operations and financial health |
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if the laws are violated. |
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- GameStop Corp. offers games and entertainment products through its stores and |
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ecommerce platforms. |
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- Stribild is an oral formulation dosed once a day for the treatment of HIV-1 infection |
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in certain patients. |
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- source_sentence: How might a 10% change in the obsolescence reserve percentage impact |
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net earnings? |
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sentences: |
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- A 10% change in our obsolescence reserve percentage at January 28, 2023 would |
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have affected net earnings by approximately $2.5 million in fiscal 2022. |
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- The information required by Item 3 on Legal Proceedings is provided by referencing |
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Note 19 of the Notes to Consolidated Financial Statements in Item 8. |
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- ured notes for an aggregate principal amount of $18.50 billion. These notes were |
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issued in multiple series, which mature from 2027 through 2063. |
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- source_sentence: What are the SEC's regulations for security-based swap dealers |
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like Goldman Sachs' subsidiaries? |
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sentences: |
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- The increase in other income, net was primarily due to an increase in interest |
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income as a result of higher cash balances and higher interest rates. |
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- Through our Stubs loyalty programs, we have developed a consumer database of approximately |
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32 million households, representing approximately 64 million individuals. |
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- SEC rules govern the registration and regulation of security-based swap dealers. |
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Security-based swaps are defined as swaps on single securities, single loans or |
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narrow-based baskets or indices of securities. The SEC has adopted a number of |
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rules for security-based swap dealers, including (i) capital, margin and segregation |
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requirements; (ii) record-keeping, financial reporting and notification requirements; |
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(iii) business conduct standards; (iv) regulatory and public trade reporting; |
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and (v) the application of risk mitigation techniques to uncleared portfolios |
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of security-based swaps. |
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- source_sentence: How is the information about legal proceedings organized in the |
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financial documents according to the provided context? |
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sentences: |
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- The information about legal proceedings is organized under Part II, Item 8 in |
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the section titled 'Financial Statements and Supplementary Data – Note 14'. |
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- We have a match-funding policy that addresses the interest rate risk by aligning |
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the interest rate profile (fixed or floating rate and duration) of our debt portfolio |
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with the interest rate profile of our finance receivable portfolio within a predetermined |
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range on an ongoing basis. In connection with that policy, we use interest rate |
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derivative instruments to modify the debt structure to match assets within the |
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finance receivable portfolio. |
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- Achieved adjusted FIFO operating profit of $5.1 billion, which represents an 18% |
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increase compared to 2021. |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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model-index: |
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- name: Nomic Financial Matryoshka |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 768 |
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type: dim_768 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.7457142857142857 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8614285714285714 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8957142857142857 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.93 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7457142857142857 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.28714285714285714 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.1791428571428571 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09299999999999999 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7457142857142857 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8614285714285714 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8957142857142857 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.93 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8398915226132163 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.8107896825396824 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.8136819482601757 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 512 |
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type: dim_512 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.7357142857142858 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8514285714285714 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8914285714285715 |
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|
name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.93 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7357142857142858 |
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|
name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2838095238095238 |
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name: Cosine Precision@3 |
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|
- type: cosine_precision@5 |
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value: 0.17828571428571427 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09299999999999999 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7357142857142858 |
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|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8514285714285714 |
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|
name: Cosine Recall@3 |
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|
- type: cosine_recall@5 |
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value: 0.8914285714285715 |
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|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.93 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8352581932886503 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.8047103174603173 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.8075415578285141 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 256 |
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type: dim_256 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.7285714285714285 |
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|
name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8614285714285714 |
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|
name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8857142857142857 |
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|
name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9271428571428572 |
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|
name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7285714285714285 |
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|
name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.28714285714285714 |
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|
name: Cosine Precision@3 |
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|
- type: cosine_precision@5 |
|
|
value: 0.17714285714285713 |
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|
name: Cosine Precision@5 |
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|
- type: cosine_precision@10 |
|
|
value: 0.09271428571428571 |
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|
name: Cosine Precision@10 |
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|
- type: cosine_recall@1 |
|
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value: 0.7285714285714285 |
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|
name: Cosine Recall@1 |
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|
- type: cosine_recall@3 |
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value: 0.8614285714285714 |
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|
name: Cosine Recall@3 |
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|
- type: cosine_recall@5 |
|
|
value: 0.8857142857142857 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.9271428571428572 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.8319809230146766 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
|
value: 0.8011235827664398 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
|
value: 0.8040552556779361 |
|
|
name: Cosine Map@100 |
|
|
- task: |
|
|
type: information-retrieval |
|
|
name: Information Retrieval |
|
|
dataset: |
|
|
name: dim 128 |
|
|
type: dim_128 |
|
|
metrics: |
|
|
- type: cosine_accuracy@1 |
|
|
value: 0.7128571428571429 |
|
|
name: Cosine Accuracy@1 |
|
|
- type: cosine_accuracy@3 |
|
|
value: 0.8328571428571429 |
|
|
name: Cosine Accuracy@3 |
|
|
- type: cosine_accuracy@5 |
|
|
value: 0.8671428571428571 |
|
|
name: Cosine Accuracy@5 |
|
|
- type: cosine_accuracy@10 |
|
|
value: 0.9142857142857143 |
|
|
name: Cosine Accuracy@10 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.7128571428571429 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@3 |
|
|
value: 0.2776190476190476 |
|
|
name: Cosine Precision@3 |
|
|
- type: cosine_precision@5 |
|
|
value: 0.1734285714285714 |
|
|
name: Cosine Precision@5 |
|
|
- type: cosine_precision@10 |
|
|
value: 0.09142857142857141 |
|
|
name: Cosine Precision@10 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.7128571428571429 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
|
value: 0.8328571428571429 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
|
value: 0.8671428571428571 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.9142857142857143 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.8145627876253931 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
|
value: 0.7825572562358278 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
|
value: 0.7859620809117356 |
|
|
name: Cosine Map@100 |
|
|
- task: |
|
|
type: information-retrieval |
|
|
name: Information Retrieval |
|
|
dataset: |
|
|
name: dim 64 |
|
|
type: dim_64 |
|
|
metrics: |
|
|
- type: cosine_accuracy@1 |
|
|
value: 0.6642857142857143 |
|
|
name: Cosine Accuracy@1 |
|
|
- type: cosine_accuracy@3 |
|
|
value: 0.8042857142857143 |
|
|
name: Cosine Accuracy@3 |
|
|
- type: cosine_accuracy@5 |
|
|
value: 0.8457142857142858 |
|
|
name: Cosine Accuracy@5 |
|
|
- type: cosine_accuracy@10 |
|
|
value: 0.9028571428571428 |
|
|
name: Cosine Accuracy@10 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.6642857142857143 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@3 |
|
|
value: 0.2680952380952381 |
|
|
name: Cosine Precision@3 |
|
|
- type: cosine_precision@5 |
|
|
value: 0.16914285714285712 |
|
|
name: Cosine Precision@5 |
|
|
- type: cosine_precision@10 |
|
|
value: 0.09028571428571427 |
|
|
name: Cosine Precision@10 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.6642857142857143 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
|
value: 0.8042857142857143 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
|
value: 0.8457142857142858 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.9028571428571428 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.7821373629924483 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
|
value: 0.7436649659863942 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
|
value: 0.7468498882402747 |
|
|
name: Cosine Map@100 |
|
|
--- |
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# Nomic Financial Matryoshka |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
|
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### Model Description |
|
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- **Model Type:** Sentence Transformer |
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|
- **Base model:** [nomic-ai/nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1) <!-- at revision eb6b20cd65fcbdf7a2bc4ebac97908b3b21da981 --> |
|
|
- **Maximum Sequence Length:** 8192 tokens |
|
|
- **Output Dimensionality:** 768 tokens |
|
|
- **Similarity Function:** Cosine Similarity |
|
|
- **Training Dataset:** |
|
|
- json |
|
|
- **Language:** en |
|
|
- **License:** apache-2.0 |
|
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|
|
|
### Model Sources |
|
|
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
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|
|
|
### Full Model Architecture |
|
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|
|
|
``` |
|
|
SentenceTransformer( |
|
|
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel |
|
|
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
|
(2): Normalize() |
|
|
) |
|
|
``` |
|
|
|
|
|
## Usage |
|
|
|
|
|
### Direct Usage (Sentence Transformers) |
|
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|
|
|
First install the Sentence Transformers library: |
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|
|
|
```bash |
|
|
pip install -U sentence-transformers |
|
|
``` |
|
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|
|
|
Then you can load this model and run inference. |
|
|
```python |
|
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from sentence_transformers import SentenceTransformer |
|
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|
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# Download from the 🤗 Hub |
|
|
model = SentenceTransformer("aniket0898/bge-base-financial-matryoshka") |
|
|
# Run inference |
|
|
sentences = [ |
|
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'How is the information about legal proceedings organized in the financial documents according to the provided context?', |
|
|
"The information about legal proceedings is organized under Part II, Item 8 in the section titled 'Financial Statements and Supplementary Data – Note 14'.", |
|
|
'We have a match-funding policy that addresses the interest rate risk by aligning the interest rate profile (fixed or floating rate and duration) of our debt portfolio with the interest rate profile of our finance receivable portfolio within a predetermined range on an ongoing basis. In connection with that policy, we use interest rate derivative instruments to modify the debt structure to match assets within the finance receivable portfolio.', |
|
|
] |
|
|
embeddings = model.encode(sentences) |
|
|
print(embeddings.shape) |
|
|
# [3, 768] |
|
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|
|
|
# Get the similarity scores for the embeddings |
|
|
similarities = model.similarity(embeddings, embeddings) |
|
|
print(similarities.shape) |
|
|
# [3, 3] |
|
|
``` |
|
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|
|
<!-- |
|
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### Direct Usage (Transformers) |
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|
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
|
|
--> |
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<!-- |
|
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### Downstream Usage (Sentence Transformers) |
|
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|
|
|
You can finetune this model on your own dataset. |
|
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|
|
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Information Retrieval |
|
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* Dataset: `dim_768` |
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|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
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|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.7457 | |
|
|
| cosine_accuracy@3 | 0.8614 | |
|
|
| cosine_accuracy@5 | 0.8957 | |
|
|
| cosine_accuracy@10 | 0.93 | |
|
|
| cosine_precision@1 | 0.7457 | |
|
|
| cosine_precision@3 | 0.2871 | |
|
|
| cosine_precision@5 | 0.1791 | |
|
|
| cosine_precision@10 | 0.093 | |
|
|
| cosine_recall@1 | 0.7457 | |
|
|
| cosine_recall@3 | 0.8614 | |
|
|
| cosine_recall@5 | 0.8957 | |
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| cosine_recall@10 | 0.93 | |
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|
| cosine_ndcg@10 | 0.8399 | |
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| cosine_mrr@10 | 0.8108 | |
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| **cosine_map@100** | **0.8137** | |
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#### Information Retrieval |
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* Dataset: `dim_512` |
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|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
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|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.7357 | |
|
|
| cosine_accuracy@3 | 0.8514 | |
|
|
| cosine_accuracy@5 | 0.8914 | |
|
|
| cosine_accuracy@10 | 0.93 | |
|
|
| cosine_precision@1 | 0.7357 | |
|
|
| cosine_precision@3 | 0.2838 | |
|
|
| cosine_precision@5 | 0.1783 | |
|
|
| cosine_precision@10 | 0.093 | |
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|
| cosine_recall@1 | 0.7357 | |
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|
| cosine_recall@3 | 0.8514 | |
|
|
| cosine_recall@5 | 0.8914 | |
|
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| cosine_recall@10 | 0.93 | |
|
|
| cosine_ndcg@10 | 0.8353 | |
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| cosine_mrr@10 | 0.8047 | |
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| **cosine_map@100** | **0.8075** | |
|
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|
|
#### Information Retrieval |
|
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* Dataset: `dim_256` |
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
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|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
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| cosine_accuracy@1 | 0.7286 | |
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|
| cosine_accuracy@3 | 0.8614 | |
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| cosine_accuracy@5 | 0.8857 | |
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| cosine_accuracy@10 | 0.9271 | |
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| cosine_precision@1 | 0.7286 | |
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| cosine_precision@3 | 0.2871 | |
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| cosine_precision@5 | 0.1771 | |
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| cosine_precision@10 | 0.0927 | |
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| cosine_recall@1 | 0.7286 | |
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| cosine_recall@3 | 0.8614 | |
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| cosine_recall@5 | 0.8857 | |
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| cosine_recall@10 | 0.9271 | |
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| cosine_ndcg@10 | 0.832 | |
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| cosine_mrr@10 | 0.8011 | |
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| **cosine_map@100** | **0.8041** | |
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|
|
|
#### Information Retrieval |
|
|
* Dataset: `dim_128` |
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
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|
|
| Metric | Value | |
|
|
|:--------------------|:----------| |
|
|
| cosine_accuracy@1 | 0.7129 | |
|
|
| cosine_accuracy@3 | 0.8329 | |
|
|
| cosine_accuracy@5 | 0.8671 | |
|
|
| cosine_accuracy@10 | 0.9143 | |
|
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| cosine_precision@1 | 0.7129 | |
|
|
| cosine_precision@3 | 0.2776 | |
|
|
| cosine_precision@5 | 0.1734 | |
|
|
| cosine_precision@10 | 0.0914 | |
|
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| cosine_recall@1 | 0.7129 | |
|
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| cosine_recall@3 | 0.8329 | |
|
|
| cosine_recall@5 | 0.8671 | |
|
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| cosine_recall@10 | 0.9143 | |
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| cosine_ndcg@10 | 0.8146 | |
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| cosine_mrr@10 | 0.7826 | |
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| **cosine_map@100** | **0.786** | |
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#### Information Retrieval |
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* Dataset: `dim_64` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
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| Metric | Value | |
|
|
|:--------------------|:-----------| |
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|
| cosine_accuracy@1 | 0.6643 | |
|
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| cosine_accuracy@3 | 0.8043 | |
|
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| cosine_accuracy@5 | 0.8457 | |
|
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| cosine_accuracy@10 | 0.9029 | |
|
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| cosine_precision@1 | 0.6643 | |
|
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| cosine_precision@3 | 0.2681 | |
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| cosine_precision@5 | 0.1691 | |
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| cosine_precision@10 | 0.0903 | |
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| cosine_recall@1 | 0.6643 | |
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| cosine_recall@3 | 0.8043 | |
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| cosine_recall@5 | 0.8457 | |
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| cosine_recall@10 | 0.9029 | |
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| cosine_ndcg@10 | 0.7821 | |
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| cosine_mrr@10 | 0.7437 | |
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| **cosine_map@100** | **0.7468** | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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|
### Training Dataset |
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#### json |
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* Dataset: json |
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* Size: 6,300 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 1000 samples: |
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|
| | anchor | positive | |
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|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 2 tokens</li><li>mean: 20.47 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 45.09 tokens</li><li>max: 272 tokens</li></ul> | |
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|
* Samples: |
|
|
| anchor | positive | |
|
|
|:-------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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|
| <code>What was the stored value of cards and loyalty program balances at the end of fiscal year 2022?</code> | <code>Stored value cards and loyalty program at October 2, 2022 showed a balance of approximately $1.503 billion.</code> | |
|
|
| <code>What transformation is planned for Le Jardin located at The Londoner Macao?</code> | <code>Le Jardin, located on the southern flank of The Londoner Macao, is to undergo a transformation into a distinctive garden-themed attraction spanning approximately 50,000 square meters.</code> | |
|
|
| <code>What are the key terms of the new Labor Agreement ratified by the UAW in 2023?</code> | <code>The key terms and provisions of the Labor Agreement are: General wage increases of 11% upon ratification in 2023, 3% in September each of 2024, 2025 and 2026, and 5% in September 2027; Consolidation of applicable wage classifications for in-progression, temporary and other employees – with employees reaching the top classification rate upon the completion of 156 weeks of active service; The re-establishment of a cost-of-living allowance; Lump sum ratification bonus payments of $5,000 paid to eligible employees in the three months ended December 31, 2023; For members currently employed and enrolled in the Employees’ Pension Plan, an increase of $5.00 to the monthly basic benefit for past and future service provided; A 3.6% increase in company contributions to eligible employees' defined contribution retirement accounts; and Annual contribution of $500 to eligible retirees or surviving spouses.</code> | |
|
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"loss": "MultipleNegativesRankingLoss", |
|
|
"matryoshka_dims": [ |
|
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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|
} |
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|
``` |
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|
|
### 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`: 4 |
|
|
- `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 |
|
|
<details><summary>Click to expand</summary> |
|
|
|
|
|
- `overwrite_output_dir`: False |
|
|
- `do_predict`: False |
|
|
- `eval_strategy`: epoch |
|
|
- `prediction_loss_only`: True |
|
|
- `per_device_train_batch_size`: 32 |
|
|
- `per_device_eval_batch_size`: 16 |
|
|
- `per_gpu_train_batch_size`: None |
|
|
- `per_gpu_eval_batch_size`: None |
|
|
- `gradient_accumulation_steps`: 16 |
|
|
- `eval_accumulation_steps`: None |
|
|
- `learning_rate`: 2e-05 |
|
|
- `weight_decay`: 0.0 |
|
|
- `adam_beta1`: 0.9 |
|
|
- `adam_beta2`: 0.999 |
|
|
- `adam_epsilon`: 1e-08 |
|
|
- `max_grad_norm`: 1.0 |
|
|
- `num_train_epochs`: 4 |
|
|
- `max_steps`: -1 |
|
|
- `lr_scheduler_type`: cosine |
|
|
- `lr_scheduler_kwargs`: {} |
|
|
- `warmup_ratio`: 0.1 |
|
|
- `warmup_steps`: 0 |
|
|
- `log_level`: passive |
|
|
- `log_level_replica`: warning |
|
|
- `log_on_each_node`: True |
|
|
- `logging_nan_inf_filter`: True |
|
|
- `save_safetensors`: True |
|
|
- `save_on_each_node`: False |
|
|
- `save_only_model`: False |
|
|
- `restore_callback_states_from_checkpoint`: False |
|
|
- `no_cuda`: False |
|
|
- `use_cpu`: False |
|
|
- `use_mps_device`: False |
|
|
- `seed`: 42 |
|
|
- `data_seed`: None |
|
|
- `jit_mode_eval`: False |
|
|
- `use_ipex`: False |
|
|
- `bf16`: True |
|
|
- `fp16`: False |
|
|
- `fp16_opt_level`: O1 |
|
|
- `half_precision_backend`: auto |
|
|
- `bf16_full_eval`: False |
|
|
- `fp16_full_eval`: False |
|
|
- `tf32`: True |
|
|
- `local_rank`: 0 |
|
|
- `ddp_backend`: None |
|
|
- `tpu_num_cores`: None |
|
|
- `tpu_metrics_debug`: False |
|
|
- `debug`: [] |
|
|
- `dataloader_drop_last`: False |
|
|
- `dataloader_num_workers`: 0 |
|
|
- `dataloader_prefetch_factor`: None |
|
|
- `past_index`: -1 |
|
|
- `disable_tqdm`: False |
|
|
- `remove_unused_columns`: True |
|
|
- `label_names`: None |
|
|
- `load_best_model_at_end`: True |
|
|
- `ignore_data_skip`: False |
|
|
- `fsdp`: [] |
|
|
- `fsdp_min_num_params`: 0 |
|
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
|
- `deepspeed`: None |
|
|
- `label_smoothing_factor`: 0.0 |
|
|
- `optim`: adamw_torch_fused |
|
|
- `optim_args`: None |
|
|
- `adafactor`: False |
|
|
- `group_by_length`: False |
|
|
- `length_column_name`: length |
|
|
- `ddp_find_unused_parameters`: None |
|
|
- `ddp_bucket_cap_mb`: None |
|
|
- `ddp_broadcast_buffers`: False |
|
|
- `dataloader_pin_memory`: True |
|
|
- `dataloader_persistent_workers`: False |
|
|
- `skip_memory_metrics`: True |
|
|
- `use_legacy_prediction_loop`: False |
|
|
- `push_to_hub`: False |
|
|
- `resume_from_checkpoint`: None |
|
|
- `hub_model_id`: None |
|
|
- `hub_strategy`: every_save |
|
|
- `hub_private_repo`: False |
|
|
- `hub_always_push`: False |
|
|
- `gradient_checkpointing`: False |
|
|
- `gradient_checkpointing_kwargs`: None |
|
|
- `include_inputs_for_metrics`: False |
|
|
- `eval_do_concat_batches`: True |
|
|
- `fp16_backend`: auto |
|
|
- `push_to_hub_model_id`: None |
|
|
- `push_to_hub_organization`: None |
|
|
- `mp_parameters`: |
|
|
- `auto_find_batch_size`: False |
|
|
- `full_determinism`: False |
|
|
- `torchdynamo`: None |
|
|
- `ray_scope`: last |
|
|
- `ddp_timeout`: 1800 |
|
|
- `torch_compile`: False |
|
|
- `torch_compile_backend`: None |
|
|
- `torch_compile_mode`: None |
|
|
- `dispatch_batches`: None |
|
|
- `split_batches`: None |
|
|
- `include_tokens_per_second`: False |
|
|
- `include_num_input_tokens_seen`: False |
|
|
- `neftune_noise_alpha`: None |
|
|
- `optim_target_modules`: None |
|
|
- `batch_eval_metrics`: False |
|
|
- `batch_sampler`: no_duplicates |
|
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | Training Loss | dim_768_cosine_map@100 | dim_512_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 | |
|
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
|
| 0.8122 | 10 | 0.7331 | - | - | - | - | - | |
|
|
| 0.9746 | 12 | - | 0.7871 | 0.7796 | 0.7747 | 0.7546 | 0.7214 | |
|
|
| 1.6244 | 20 | 0.2506 | - | - | - | - | - | |
|
|
| 1.9492 | 24 | - | 0.8021 | 0.7990 | 0.7869 | 0.7691 | 0.7371 | |
|
|
| 2.4365 | 30 | 0.1029 | - | - | - | - | - | |
|
|
| 2.9239 | 36 | - | 0.8030 | 0.8017 | 0.7926 | 0.7760 | 0.7402 | |
|
|
| 3.2487 | 40 | 0.054 | - | - | - | - | - | |
|
|
| **3.8985** | **48** | **-** | **0.8055** | **0.799** | **0.7924** | **0.7754** | **0.7383** | |
|
|
| 0.8122 | 10 | 0.0397 | - | - | - | - | - | |
|
|
| 0.9746 | 12 | - | 0.8109 | 0.7983 | 0.7974 | 0.7795 | 0.7373 | |
|
|
| 1.6244 | 20 | 0.0301 | - | - | - | - | - | |
|
|
| 1.9492 | 24 | - | 0.8115 | 0.8049 | 0.8026 | 0.7839 | 0.7486 | |
|
|
| 2.4365 | 30 | 0.0236 | - | - | - | - | - | |
|
|
| 2.9239 | 36 | - | 0.8138 | 0.8082 | 0.8045 | 0.7858 | 0.7470 | |
|
|
| 3.2487 | 40 | 0.0131 | - | - | - | - | - | |
|
|
| **3.8985** | **48** | **-** | **0.8137** | **0.8075** | **0.8041** | **0.786** | **0.7468** | |
|
|
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.8.10 |
|
|
- Sentence Transformers: 3.2.1 |
|
|
- Transformers: 4.41.2 |
|
|
- PyTorch: 2.1.2+cu121 |
|
|
- Accelerate: 1.0.1 |
|
|
- Datasets: 2.19.1 |
|
|
- Tokenizers: 0.19.1 |
|
|
|
|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
|
|
|
#### Sentence Transformers |
|
|
```bibtex |
|
|
@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 |
|
|
```bibtex |
|
|
@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 |
|
|
```bibtex |
|
|
@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} |
|
|
} |
|
|
``` |
|
|
|
|
|
<!-- |
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|
## Glossary |
|
|
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
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--> |
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|
|
<!-- |
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## Model Card Authors |
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|
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|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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<!-- |
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## Model Card Contact |
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