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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: BAAI/bge-base-en-v1.5 |
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widget: |
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- source_sentence: 'The fair value of consideration transferred of $212.1 million |
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consisted of: (1) cash consideration paid of $211.3 million, net of cash acquired, |
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and (2) non-cash consideration of $0.8 million representing the portion of the |
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replacement equity awards issued in connection with the acquisition that was associated |
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with services rendered through the date of the acquisition.' |
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sentences: |
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- What is the monthly cost of a Connected Fitness Subscription if it includes a |
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combination of a Bike, Tread, Guide, or Row product in the same household as of |
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June 2022? |
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- What was the fair value of the total consideration transferred for the acquisition |
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discussed, and how was it composed? |
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- How did the Tax Court rule on November 18, 2020, regarding the company's dispute |
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with the IRS? |
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- source_sentence: Each of the UK LSA members has agreed, on a several and not joint |
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basis, to compensate the Company for certain losses which may be incurred by the |
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Company, Visa Europe or their affiliates as a result of certain existing and potential |
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litigation relating to the setting and implementation of domestic multilateral |
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interchange fee rates in the United Kingdom prior to the closing of the Visa Europe |
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acquisition (Closing), subject to the terms and conditions set forth therein and, |
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with respect to each UK LSA member, up to a maximum amount of the up-front cash |
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consideration received by such UK LSA member. The UK LSA members’ obligations |
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under the UK loss sharing agreement are conditional upon, among other things, |
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either (a) losses valued in excess of the sterling equivalent on June 21, 2016 |
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of €1.0 billion having arisen in UK covered claims (and such losses having reduced |
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the conversion rate of the series B preferred stock accordingly), or (b) the conversion |
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rate of the series B preferred stock having been reduced to zero pursuant to losses |
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arising in claims... |
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sentences: |
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- Are AbbVie's corporate governance materials available to the public, and if so, |
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where? |
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- What conditions must be met for the UK loss sharing agreement to compensate for |
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losses? |
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- How much did Delta Air Lines recognize in government grants from the Payroll Support |
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Programs during the year ended December 31, 2021? |
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- source_sentence: We provide our customers with an opportunity to trade-in their |
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pre-owned gaming, mobility, and other products at our stores in exchange for cash |
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or credit which can be applied towards the purchase of other products. |
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sentences: |
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- What is GameStop's trade-in program? |
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- What were the total unrealized losses on U.S. Treasury securities as of the last |
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reporting date? |
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- What methods can a refinery use to meet its Environmental Protection Agency (EPA) |
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requirements for blending renewable fuels? |
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- source_sentence: Diluted earnings per share is calculated using our weighted-average |
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outstanding common shares including the dilutive effect of stock awards as determined |
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under the treasury stock method. |
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sentences: |
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- How do changes in the assumed long-term rate of return affect AbbVie's net periodic |
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benefit cost for pension plans? |
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- What are the primary factors discussed in the Management’s Discussion and Analysis |
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that affect the financial statements year-to-year changes? |
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- What is the method used to calculate diluted earnings per share? |
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- source_sentence: Item 8 in the document covers 'Financial Statements and Supplementary |
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Data'. |
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sentences: |
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- What type of information does Item 8 in the document cover? |
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- What are some of the potential consequences for Meta Platforms, Inc. from inquiries |
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or investigations as noted in the provided text? |
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- How is the take rate calculated and what does it represent? |
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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: BGE base 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: 0.68 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8242857142857143 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
|
value: 0.8571428571428571 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.8985714285714286 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.68 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.27476190476190476 |
|
name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.1714285714285714 |
|
name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.08985714285714284 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.68 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8242857142857143 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8571428571428571 |
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.8985714285714286 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7931022011968226 |
|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
|
value: 0.759021541950113 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
|
value: 0.7627727073081649 |
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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 |
|
value: 0.6685714285714286 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.82 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
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value: 0.86 |
|
name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9042857142857142 |
|
name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
|
value: 0.6685714285714286 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2733333333333333 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.172 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
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value: 0.09042857142857141 |
|
name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.6685714285714286 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.82 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.86 |
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name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
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value: 0.9042857142857142 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7907009828560375 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7540430839002267 |
|
name: Cosine Mrr@10 |
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- type: cosine_map@100 |
|
value: 0.7572918009226873 |
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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 |
|
value: 0.6771428571428572 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8142857142857143 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8571428571428571 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8857142857142857 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6771428571428572 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2714285714285714 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1714285714285714 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08857142857142855 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6771428571428572 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8142857142857143 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8571428571428571 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8857142857142857 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7870155634206691 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7548027210884352 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7592885578023618 |
|
name: Cosine Map@100 |
|
- task: |
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type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6542857142857142 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8071428571428572 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8514285714285714 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8857142857142857 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6542857142857142 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.26904761904761904 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17028571428571426 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08857142857142856 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6542857142857142 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8071428571428572 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8514285714285714 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8857142857142857 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7751084647376248 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.73912925170068 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7430473786684797 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6157142857142858 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7771428571428571 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8214285714285714 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8728571428571429 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6157142857142858 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.259047619047619 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16428571428571428 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08728571428571427 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6157142857142858 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7771428571428571 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8214285714285714 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8728571428571429 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7472883962433147 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7067517006802716 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7111439006196084 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# BGE base Financial Matryoshka |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) 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 |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 dimensions |
|
- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
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- json |
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- **Language:** en |
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- **License:** apache-2.0 |
|
|
|
### 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) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
|
(1): Pooling({'word_embedding_dimension': 768, '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}) |
|
(2): Normalize() |
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) |
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``` |
|
|
|
## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("Shivam1311/bge-base-financial-matryoshka") |
|
# Run inference |
|
sentences = [ |
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"Item 8 in the document covers 'Financial Statements and Supplementary Data'.", |
|
'What type of information does Item 8 in the document cover?', |
|
'What are some of the potential consequences for Meta Platforms, Inc. from inquiries or investigations as noted in the provided text?', |
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] |
|
embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
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|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
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|
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</details> |
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--> |
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|
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<!-- |
|
### Downstream Usage (Sentence Transformers) |
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|
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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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<!-- |
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### Out-of-Scope Use |
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|
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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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|
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## Evaluation |
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|
|
### Metrics |
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|
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#### Information Retrieval |
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|
|
* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `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 | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | |
|
|:--------------------|:-----------|:-----------|:----------|:-----------|:-----------| |
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| cosine_accuracy@1 | 0.68 | 0.6686 | 0.6771 | 0.6543 | 0.6157 | |
|
| cosine_accuracy@3 | 0.8243 | 0.82 | 0.8143 | 0.8071 | 0.7771 | |
|
| cosine_accuracy@5 | 0.8571 | 0.86 | 0.8571 | 0.8514 | 0.8214 | |
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| cosine_accuracy@10 | 0.8986 | 0.9043 | 0.8857 | 0.8857 | 0.8729 | |
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| cosine_precision@1 | 0.68 | 0.6686 | 0.6771 | 0.6543 | 0.6157 | |
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| cosine_precision@3 | 0.2748 | 0.2733 | 0.2714 | 0.269 | 0.259 | |
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| cosine_precision@5 | 0.1714 | 0.172 | 0.1714 | 0.1703 | 0.1643 | |
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| cosine_precision@10 | 0.0899 | 0.0904 | 0.0886 | 0.0886 | 0.0873 | |
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| cosine_recall@1 | 0.68 | 0.6686 | 0.6771 | 0.6543 | 0.6157 | |
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| cosine_recall@3 | 0.8243 | 0.82 | 0.8143 | 0.8071 | 0.7771 | |
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| cosine_recall@5 | 0.8571 | 0.86 | 0.8571 | 0.8514 | 0.8214 | |
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| cosine_recall@10 | 0.8986 | 0.9043 | 0.8857 | 0.8857 | 0.8729 | |
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| **cosine_ndcg@10** | **0.7931** | **0.7907** | **0.787** | **0.7751** | **0.7473** | |
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| cosine_mrr@10 | 0.759 | 0.754 | 0.7548 | 0.7391 | 0.7068 | |
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| cosine_map@100 | 0.7628 | 0.7573 | 0.7593 | 0.743 | 0.7111 | |
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|
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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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--> |
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<!-- |
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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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|
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## Training Details |
|
|
|
### Training Dataset |
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|
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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>positive</code> and <code>anchor</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 8 tokens</li><li>mean: 46.61 tokens</li><li>max: 439 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.72 tokens</li><li>max: 51 tokens</li></ul> | |
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* Samples: |
|
| positive | anchor | |
|
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------| |
|
| <code>Operating costs and expenses increased $80.3 million, or 7.1%, during the year ended December 31, 2023, compared to the year ended December 31, 2022 primarily due to increases in film exhibition and food and beverage costs.</code> | <code>What factors contributed to the escalation in operating costs and expenses in 2023?</code> | |
|
| <code>In the United States, the company purchases HFCS to meet its and its bottlers’ requirements with the assistance of Coca-Cola Bottlers’ Sales & Services Company LLC, which is a procurement service provider for their North American operations.</code> | <code>How does the company source high fructose corn syrup (HFCS) in the United States?</code> | |
|
| <code>Item 8. Financial Statements and Supplementary Data The index to Financial Statements and Supplementary Data is presented</code> | <code>What is presented in Item 8 according to Financial Statements and Supplementary Data?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
768, |
|
512, |
|
256, |
|
128, |
|
64 |
|
], |
|
"matryoshka_weights": [ |
|
1, |
|
1, |
|
1, |
|
1, |
|
1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_train_batch_size`: 16 |
|
- `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`: False |
|
- `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`: 16 |
|
- `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 |
|
- `torch_empty_cache_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`: False |
|
- `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`: None |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `include_for_metrics`: [] |
|
- `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 |
|
- `eval_on_start`: False |
|
- `use_liger_kernel`: False |
|
- `eval_use_gather_object`: False |
|
- `average_tokens_across_devices`: False |
|
- `prompts`: None |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |
|
|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
| 0.4061 | 10 | 16.0873 | - | - | - | - | - | |
|
| 0.8122 | 20 | 8.3282 | - | - | - | - | - | |
|
| 1.0 | 25 | - | 0.7841 | 0.7796 | 0.7774 | 0.7631 | 0.7320 | |
|
| 1.2030 | 30 | 5.1781 | - | - | - | - | - | |
|
| 1.6091 | 40 | 4.0947 | - | - | - | - | - | |
|
| 2.0 | 50 | 3.9824 | 0.7888 | 0.7867 | 0.7851 | 0.7701 | 0.7401 | |
|
| 2.4061 | 60 | 2.854 | - | - | - | - | - | |
|
| 2.8122 | 70 | 2.9878 | - | - | - | - | - | |
|
| **3.0** | **75** | **-** | **0.7913** | **0.7903** | **0.7869** | **0.7755** | **0.7469** | |
|
| 3.2030 | 80 | 2.5653 | - | - | - | - | - | |
|
| 3.6091 | 90 | 2.999 | - | - | - | - | - | |
|
| 3.8528 | 96 | - | 0.7931 | 0.7907 | 0.7870 | 0.7751 | 0.7473 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.11.11 |
|
- Sentence Transformers: 3.4.1 |
|
- Transformers: 4.48.3 |
|
- PyTorch: 2.5.1+cu124 |
|
- Accelerate: 1.3.0 |
|
- Datasets: 3.3.2 |
|
- Tokenizers: 0.21.0 |
|
|
|
## 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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