Add new SentenceTransformer model.

README.md

@@ -27,54 +27,53 @@ tags:
 - sentence-similarity
 - feature-extraction
 - generated_from_trainer
-- dataset_size:580
+- dataset_size:3550
 - loss:MatryoshkaLoss
 - loss:MultipleNegativesRankingLoss
 widget:
-- source_sentence: In response to hypothetical economic scenarios presented by the
-    Federal Reserve, Wells Fargo formulated a capital action plan. This was done as
-    a part of the CCAR (Comprehensive Capital Analysis and Review) process. The scenarios
-    tested included a hypothetical severe global recession which, at its most stressful
-    point, reduces our Pre-Provision Net Revenue (PPNR) to negative levels for four
-    consecutive quarters.
+- source_sentence: At the end of 2023, Alphabet Inc. reported total debts amounting
+    to $14.2 billion, compared to $10.9 billion at the end of 2022.
   sentences:
-  - What is the proposed dividend per share for the shareholders of Apple Inc. for
-    the financial year ending in 2023?
-  - What steps has Wells Fargo undertaken to sustain in the event of a severe global
-    recession?
-  - What was the total net income for Intel in 2021?
-- source_sentence: Microsoft Corporation has been paying consistent dividends to its
-    shareholders on a quarterly basis. The company's Board of Directors reviews the
-    dividend policy on a regular basis and plans to continue paying quarterly dividends,
-    subject to capital availability and financial conditions
+  - What was the total debt of Alphabet Inc. as of the end of 2023?
+  - What was ExxonMobil's contribution to the energy production in the Energy sector
+    during 2020?
+  - Describe Amazon's revenue growth in 2023?
+- source_sentence: In 2022, Pfizer strategically managed cash flow from investments
+    by utilizing operating cash flow, issuing new debt, and through the monetization
+    of certain non-core assets. This approach of diversifying the source of funding
+    for investments was done to minimize risk and uncertainty in economic conditions.
   sentences:
-  - What did Amazon.com, Inc. anticipate regarding its free cash flows in the future?
-  - What is Tesla's outlook for 2024 in terms of vehicle production?
-  - What is Microsoft Corporation's dividend policy?
-- source_sentence: In the second quarter of 2023, Tesla's automotive revenue increased
-    by 58% compared to the same period previous year. These results were primarily
-    driven by increased vehicle deliveries and expansion in the China market.
+  - How much capital expenditure did AUX Energy invest in renewable energy projects
+    in 2022?
+  - What effect did the 2023 market downturn have on Amazon's retail and cloud segments?
+  - How did Pfizer manage cash flows from investments in 2022?
+- source_sentence: The primary revenue generators for JPMorgan Chase for the fiscal
+    year 2023 were the Corporate & Investment Bank (CIB) and the Asset & Wealth Management
+    (AWM) sectors. The CIB sector benefited from a rise in merger and acquisition
+    activities, while AWM saw large net inflows.
   sentences:
-  - What action did the Federal Reserve take to address the inflation surge in 2027?
-  - What revenue did Apple Inc. report in the first quarter of 2021?
-  - How did Tesla's automotive revenue perform in the second quarter of 2023?
-- source_sentence: Intel Corporation is an American multinational corporation and
-    technology company headquartered in Santa Clara, California. It's primarily known
-    for designing and manufacturing semiconductors and various technology solutions,
-    including processors for computer systems and servers, integrated digital technology
-    platforms, and system-on-chip units for gateways.
+  - What is General Electric's strategic priority for its Aviation business segment?
+  - Which sectors contributed the most to the revenue of JPMorgan Chase for FY 2023?
+  - What is the principal activity of Apple Inc.?
+- source_sentence: For the fiscal year 2023, Microsoft's Intelligent Cloud segment
+    generated revenues of $58 billion, demonstrating solid growth fueled by strong
+    demand for cloud services and server products.
   sentences:
-  - What is Intel's main area of business?
-  - What was the revenue growth percentage of Amazon in the second quarter of 2024?
-  - How much capital expenditure did Amazon.com report in 2025?
-- source_sentence: In 2023, EnergyCorp declared a dividend of $2.5 per share.
-  sentences:
-  - How did Amazon’s shift to one-day prime delivery affect its operational costs
+  - What is the primary strategy of McDonald’s to drive growth in the future?
+  - What impact did the increase in gold prices have on Newmont Corporation's revenue
     in 2023?
-  - What dividend did the EnergyCorp pay to its shareholders in 2023?
-  - What was the profit margin of Airbus in the year 2025?
+  - What was the revenue generated by Microsoft's Intelligent Cloud segment for fiscal
+    year 2023?
+- source_sentence: Microsoft, in their latest press release, revealed that they are
+    anticipating a revenue growth of approximately 12% for the fiscal year ending
+    in 2024.
+  sentences:
+  - What is Microsoft's projected revenue growth for fiscal year 2024?
+  - What is the fair value of equity method investments of Microsoft in the fiscal
+    year 2025?
+  - What was the impact of COVID-19 on Zoom's profits?
 model-index:
-- name: Bmixedbread-ai/mxbai-embed-large-v1 Financial Matryoshka
+- name: mxbai-embed-large-v1-financial-rag-matryoshka
   results:
   - task:
       type: information-retrieval
@@ -84,49 +83,49 @@ model-index:
       type: dim_1024
     metrics:
     - type: cosine_accuracy@1
-      value: 0.8923076923076924
+      value: 0.8455696202531645
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.9692307692307692
+      value: 0.9392405063291139
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.9692307692307692
+      value: 0.9670886075949368
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.9846153846153847
+      value: 0.9898734177215189
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.8923076923076924
+      value: 0.8455696202531645
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.32307692307692304
+      value: 0.31308016877637135
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.1938461538461538
+      value: 0.19341772151898737
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.09846153846153843
+      value: 0.0989873417721519
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.8923076923076924
+      value: 0.8455696202531645
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.9692307692307692
+      value: 0.9392405063291139
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.9692307692307692
+      value: 0.9670886075949368
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.9846153846153847
+      value: 0.9898734177215189
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.941940347600734
+      value: 0.9212281141643793
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.927838827838828
+      value: 0.898873819570022
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.928083028083028
+      value: 0.8993853803492357
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -136,49 +135,49 @@ model-index:
       type: dim_768
     metrics:
     - type: cosine_accuracy@1
-      value: 0.8923076923076924
+      value: 0.8455696202531645
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.9692307692307692
+      value: 0.9392405063291139
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.9692307692307692
+      value: 0.9670886075949368
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.9846153846153847
+      value: 0.9898734177215189
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.8923076923076924
+      value: 0.8455696202531645
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.32307692307692304
+      value: 0.3130801687763713
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.1938461538461538
+      value: 0.1934177215189873
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.09846153846153843
+      value: 0.0989873417721519
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.8923076923076924
+      value: 0.8455696202531645
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.9692307692307692
+      value: 0.9392405063291139
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.9692307692307692
+      value: 0.9670886075949368
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.9846153846153847
+      value: 0.9898734177215189
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.9422922530434215
+      value: 0.9217284365901642
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.9282051282051282
+      value: 0.8994826200522402
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.9284418145956608
+      value: 0.8999494134557425
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -188,49 +187,49 @@ model-index:
       type: dim_512
     metrics:
     - type: cosine_accuracy@1
-      value: 0.8923076923076924
+      value: 0.8405063291139241
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.9692307692307692
+      value: 0.9367088607594937
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.9692307692307692
+      value: 0.9645569620253165
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.9846153846153847
+      value: 0.9898734177215189
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.8923076923076924
+      value: 0.8405063291139241
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.32307692307692304
+      value: 0.31223628691983124
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.1938461538461538
+      value: 0.19291139240506328
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.09846153846153843
+      value: 0.0989873417721519
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.8923076923076924
+      value: 0.8405063291139241
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.9692307692307692
+      value: 0.9367088607594937
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.9692307692307692
+      value: 0.9645569620253165
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.9846153846153847
+      value: 0.9898734177215189
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.941940347600734
+      value: 0.9186273598847787
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.927838827838828
+      value: 0.8954631303998389
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.928113553113553
+      value: 0.8958871142668611
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -240,49 +239,49 @@ model-index:
       type: dim_256
     metrics:
     - type: cosine_accuracy@1
-      value: 0.8923076923076924
+      value: 0.8455696202531645
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.9692307692307692
+      value: 0.9392405063291139
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.9692307692307692
+      value: 0.9645569620253165
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.9846153846153847
+      value: 0.9898734177215189
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.8923076923076924
+      value: 0.8455696202531645
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.32307692307692304
+      value: 0.3130801687763713
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.1938461538461538
+      value: 0.19291139240506328
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.09846153846153843
+      value: 0.0989873417721519
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.8923076923076924
+      value: 0.8455696202531645
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.9692307692307692
+      value: 0.9392405063291139
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.9692307692307692
+      value: 0.9645569620253165
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.9846153846153847
+      value: 0.9898734177215189
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.9416654482692324
+      value: 0.9201161947922436
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.9275641025641026
+      value: 0.8975597749648381
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.9278846153846154
+      value: 0.8979721416614026
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -292,49 +291,49 @@ model-index:
       type: dim_128
     metrics:
     - type: cosine_accuracy@1
-      value: 0.8461538461538461
+      value: 0.8405063291139241
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.9538461538461539
+      value: 0.9417721518987342
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.9692307692307692
+      value: 0.9645569620253165
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.9846153846153847
+      value: 0.9848101265822785
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.8461538461538461
+      value: 0.8405063291139241
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.31794871794871793
+      value: 0.3139240506329114
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.1938461538461538
+      value: 0.19291139240506328
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.09846153846153843
+      value: 0.09848101265822784
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.8461538461538461
+      value: 0.8405063291139241
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.9538461538461539
+      value: 0.9417721518987342
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.9692307692307692
+      value: 0.9645569620253165
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.9846153846153847
+      value: 0.9848101265822785
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.9221774232775186
+      value: 0.9170562815583235
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.9012820512820513
+      value: 0.8948693992364878
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.9016398330351819
+      value: 0.8957325656059834
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -344,53 +343,53 @@ model-index:
       type: dim_64
     metrics:
     - type: cosine_accuracy@1
-      value: 0.8153846153846154
+      value: 0.8405063291139241
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.9692307692307692
+      value: 0.9316455696202531
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.9846153846153847
+      value: 0.9569620253164557
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.9846153846153847
+      value: 0.9822784810126582
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.8153846153846154
+      value: 0.8405063291139241
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.32307692307692304
+      value: 0.3105485232067511
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.19692307692307687
+      value: 0.19139240506329114
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.09846153846153843
+      value: 0.09822784810126582
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.8153846153846154
+      value: 0.8405063291139241
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.9692307692307692
+      value: 0.9316455696202531
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.9846153846153847
+      value: 0.9569620253164557
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.9846153846153847
+      value: 0.9822784810126582
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.9123594012651499
+      value: 0.9153318022971121
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.8876923076923079
+      value: 0.8934589109905566
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.8879622132253712
+      value: 0.8943102728098851
       name: Cosine Map@100
 ---
 
-# Bmixedbread-ai/mxbai-embed-large-v1 Financial Matryoshka
+# mxbai-embed-large-v1-financial-rag-matryoshka
 
 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1). 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.
 
@@ -439,9 +438,9 @@ from sentence_transformers import SentenceTransformer
 model = SentenceTransformer("rbhatia46/mxbai-embed-large-v1-financial-rag-matryoshka")
 # Run inference
 sentences = [
-    'In 2023, EnergyCorp declared a dividend of $2.5 per share.',
-    'What dividend did the EnergyCorp pay to its shareholders in 2023?',
-    'How did Amazon’s shift to one-day prime delivery affect its operational costs in 2023?',
+    'Microsoft, in their latest press release, revealed that they are anticipating a revenue growth of approximately 12% for the fiscal year ending in 2024.',
+    "What is Microsoft's projected revenue growth for fiscal year 2024?",
+    "What was the impact of COVID-19 on Zoom's profits?",
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -487,21 +486,21 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| cosine_accuracy@1   | 0.8923     |
-| cosine_accuracy@3   | 0.9692     |
-| cosine_accuracy@5   | 0.9692     |
-| cosine_accuracy@10  | 0.9846     |
-| cosine_precision@1  | 0.8923     |
-| cosine_precision@3  | 0.3231     |
-| cosine_precision@5  | 0.1938     |
-| cosine_precision@10 | 0.0985     |
-| cosine_recall@1     | 0.8923     |
-| cosine_recall@3     | 0.9692     |
-| cosine_recall@5     | 0.9692     |
-| cosine_recall@10    | 0.9846     |
-| cosine_ndcg@10      | 0.9419     |
-| cosine_mrr@10       | 0.9278     |
-| **cosine_map@100**  | **0.9281** |
+| cosine_accuracy@1   | 0.8456     |
+| cosine_accuracy@3   | 0.9392     |
+| cosine_accuracy@5   | 0.9671     |
+| cosine_accuracy@10  | 0.9899     |
+| cosine_precision@1  | 0.8456     |
+| cosine_precision@3  | 0.3131     |
+| cosine_precision@5  | 0.1934     |
+| cosine_precision@10 | 0.099      |
+| cosine_recall@1     | 0.8456     |
+| cosine_recall@3     | 0.9392     |
+| cosine_recall@5     | 0.9671     |
+| cosine_recall@10    | 0.9899     |
+| cosine_ndcg@10      | 0.9212     |
+| cosine_mrr@10       | 0.8989     |
+| **cosine_map@100**  | **0.8994** |
 
 #### Information Retrieval
 * Dataset: `dim_768`
@@ -509,21 +508,21 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| cosine_accuracy@1   | 0.8923     |
-| cosine_accuracy@3   | 0.9692     |
-| cosine_accuracy@5   | 0.9692     |
-| cosine_accuracy@10  | 0.9846     |
-| cosine_precision@1  | 0.8923     |
-| cosine_precision@3  | 0.3231     |
-| cosine_precision@5  | 0.1938     |
-| cosine_precision@10 | 0.0985     |
-| cosine_recall@1     | 0.8923     |
-| cosine_recall@3     | 0.9692     |
-| cosine_recall@5     | 0.9692     |
-| cosine_recall@10    | 0.9846     |
-| cosine_ndcg@10      | 0.9423     |
-| cosine_mrr@10       | 0.9282     |
-| **cosine_map@100**  | **0.9284** |
+| cosine_accuracy@1   | 0.8456     |
+| cosine_accuracy@3   | 0.9392     |
+| cosine_accuracy@5   | 0.9671     |
+| cosine_accuracy@10  | 0.9899     |
+| cosine_precision@1  | 0.8456     |
+| cosine_precision@3  | 0.3131     |
+| cosine_precision@5  | 0.1934     |
+| cosine_precision@10 | 0.099      |
+| cosine_recall@1     | 0.8456     |
+| cosine_recall@3     | 0.9392     |
+| cosine_recall@5     | 0.9671     |
+| cosine_recall@10    | 0.9899     |
+| cosine_ndcg@10      | 0.9217     |
+| cosine_mrr@10       | 0.8995     |
+| **cosine_map@100**  | **0.8999** |
 
 #### Information Retrieval
 * Dataset: `dim_512`
@@ -531,43 +530,43 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| cosine_accuracy@1   | 0.8923     |
-| cosine_accuracy@3   | 0.9692     |
-| cosine_accuracy@5   | 0.9692     |
-| cosine_accuracy@10  | 0.9846     |
-| cosine_precision@1  | 0.8923     |
-| cosine_precision@3  | 0.3231     |
-| cosine_precision@5  | 0.1938     |
-| cosine_precision@10 | 0.0985     |
-| cosine_recall@1     | 0.8923     |
-| cosine_recall@3     | 0.9692     |
-| cosine_recall@5     | 0.9692     |
-| cosine_recall@10    | 0.9846     |
-| cosine_ndcg@10      | 0.9419     |
-| cosine_mrr@10       | 0.9278     |
-| **cosine_map@100**  | **0.9281** |
+| cosine_accuracy@1   | 0.8405     |
+| cosine_accuracy@3   | 0.9367     |
+| cosine_accuracy@5   | 0.9646     |
+| cosine_accuracy@10  | 0.9899     |
+| cosine_precision@1  | 0.8405     |
+| cosine_precision@3  | 0.3122     |
+| cosine_precision@5  | 0.1929     |
+| cosine_precision@10 | 0.099      |
+| cosine_recall@1     | 0.8405     |
+| cosine_recall@3     | 0.9367     |
+| cosine_recall@5     | 0.9646     |
+| cosine_recall@10    | 0.9899     |
+| cosine_ndcg@10      | 0.9186     |
+| cosine_mrr@10       | 0.8955     |
+| **cosine_map@100**  | **0.8959** |
 
 #### Information Retrieval
 * Dataset: `dim_256`
 * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
 
-| Metric              | Value      |
-|:--------------------|:-----------|
-| cosine_accuracy@1   | 0.8923     |
-| cosine_accuracy@3   | 0.9692     |
-| cosine_accuracy@5   | 0.9692     |
-| cosine_accuracy@10  | 0.9846     |
-| cosine_precision@1  | 0.8923     |
-| cosine_precision@3  | 0.3231     |
-| cosine_precision@5  | 0.1938     |
-| cosine_precision@10 | 0.0985     |
-| cosine_recall@1     | 0.8923     |
-| cosine_recall@3     | 0.9692     |
-| cosine_recall@5     | 0.9692     |
-| cosine_recall@10    | 0.9846     |
-| cosine_ndcg@10      | 0.9417     |
-| cosine_mrr@10       | 0.9276     |
-| **cosine_map@100**  | **0.9279** |
+| Metric              | Value     |
+|:--------------------|:----------|
+| cosine_accuracy@1   | 0.8456    |
+| cosine_accuracy@3   | 0.9392    |
+| cosine_accuracy@5   | 0.9646    |
+| cosine_accuracy@10  | 0.9899    |
+| cosine_precision@1  | 0.8456    |
+| cosine_precision@3  | 0.3131    |
+| cosine_precision@5  | 0.1929    |
+| cosine_precision@10 | 0.099     |
+| cosine_recall@1     | 0.8456    |
+| cosine_recall@3     | 0.9392    |
+| cosine_recall@5     | 0.9646    |
+| cosine_recall@10    | 0.9899    |
+| cosine_ndcg@10      | 0.9201    |
+| cosine_mrr@10       | 0.8976    |
+| **cosine_map@100**  | **0.898** |
 
 #### Information Retrieval
 * Dataset: `dim_128`
@@ -575,43 +574,43 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| cosine_accuracy@1   | 0.8462     |
-| cosine_accuracy@3   | 0.9538     |
-| cosine_accuracy@5   | 0.9692     |
-| cosine_accuracy@10  | 0.9846     |
-| cosine_precision@1  | 0.8462     |
-| cosine_precision@3  | 0.3179     |
-| cosine_precision@5  | 0.1938     |
+| cosine_accuracy@1   | 0.8405     |
+| cosine_accuracy@3   | 0.9418     |
+| cosine_accuracy@5   | 0.9646     |
+| cosine_accuracy@10  | 0.9848     |
+| cosine_precision@1  | 0.8405     |
+| cosine_precision@3  | 0.3139     |
+| cosine_precision@5  | 0.1929     |
 | cosine_precision@10 | 0.0985     |
-| cosine_recall@1     | 0.8462     |
-| cosine_recall@3     | 0.9538     |
-| cosine_recall@5     | 0.9692     |
-| cosine_recall@10    | 0.9846     |
-| cosine_ndcg@10      | 0.9222     |
-| cosine_mrr@10       | 0.9013     |
-| **cosine_map@100**  | **0.9016** |
+| cosine_recall@1     | 0.8405     |
+| cosine_recall@3     | 0.9418     |
+| cosine_recall@5     | 0.9646     |
+| cosine_recall@10    | 0.9848     |
+| cosine_ndcg@10      | 0.9171     |
+| cosine_mrr@10       | 0.8949     |
+| **cosine_map@100**  | **0.8957** |
 
 #### Information Retrieval
 * Dataset: `dim_64`
 * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
 
-| Metric              | Value     |
-|:--------------------|:----------|
-| cosine_accuracy@1   | 0.8154    |
-| cosine_accuracy@3   | 0.9692    |
-| cosine_accuracy@5   | 0.9846    |
-| cosine_accuracy@10  | 0.9846    |
-| cosine_precision@1  | 0.8154    |
-| cosine_precision@3  | 0.3231    |
-| cosine_precision@5  | 0.1969    |
-| cosine_precision@10 | 0.0985    |
-| cosine_recall@1     | 0.8154    |
-| cosine_recall@3     | 0.9692    |
-| cosine_recall@5     | 0.9846    |
-| cosine_recall@10    | 0.9846    |
-| cosine_ndcg@10      | 0.9124    |
-| cosine_mrr@10       | 0.8877    |
-| **cosine_map@100**  | **0.888** |
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.8405     |
+| cosine_accuracy@3   | 0.9316     |
+| cosine_accuracy@5   | 0.957      |
+| cosine_accuracy@10  | 0.9823     |
+| cosine_precision@1  | 0.8405     |
+| cosine_precision@3  | 0.3105     |
+| cosine_precision@5  | 0.1914     |
+| cosine_precision@10 | 0.0982     |
+| cosine_recall@1     | 0.8405     |
+| cosine_recall@3     | 0.9316     |
+| cosine_recall@5     | 0.957      |
+| cosine_recall@10    | 0.9823     |
+| cosine_ndcg@10      | 0.9153     |
+| cosine_mrr@10       | 0.8935     |
+| **cosine_map@100**  | **0.8943** |
 
 <!--
 ## Bias, Risks and Limitations
@@ -632,19 +631,19 @@ You can finetune this model on your own dataset.
 #### Unnamed Dataset
 
 
-* Size: 580 training samples
+* Size: 3,550 training samples
 * 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: 16 tokens</li><li>mean: 44.21 tokens</li><li>max: 98 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 17.5 tokens</li><li>max: 30 tokens</li></ul> |
+  |         | positive                                                                            | anchor                                                                             |
+  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                             |
+  | details | <ul><li>min: 17 tokens</li><li>mean: 44.69 tokens</li><li>max: 105 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 18.26 tokens</li><li>max: 30 tokens</li></ul> |
 * Samples:
-  | positive                                                                                                                                                                                    | anchor                                                                                      |
-  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------|
-  | <code>For the fiscal year 2020, Microsoft Corporation reported a net income of $44.3 billion, showing a 13% increase from the previous year.</code>                                         | <code>What was the net income of Microsoft Corporation for the fiscal year 2020?</code>     |
-  | <code>As of the latest financial report, Amazon has a current price to earnings ratio (P/E ratio) of 76.6.</code>                                                                           | <code>What is Amazon's current P/E ratio according to their latest financial report?</code> |
-  | <code>Microsoft Corporation posted an EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) margin of approximately 47% in 2021, showcasing strong profitability.</code> | <code>What was Microsoft Corporation's EBITDA margin in 2021?</code>                        |
+  | positive                                                                                                                                                                                                              | anchor                                                                                          |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|
+  | <code>The total revenue for Google as of 2021 stands at approximately $181 billion, primarily driven by the performance of its advertising and cloud segments, hailing from the Information Technology sector.</code> | <code>What is the total revenue of Google as of 2021?</code>                                    |
+  | <code>In Q4 2021, Amazon.com Inc. reported a significant increase in net income, reaching $14.3 billion, due to the surge in online shopping during the pandemic.</code>                                              | <code>What was the Net Income of Amazon.com Inc. in Q4 2021?</code>                             |
+  | <code>Coca-Cola reported full-year 2021 revenue of $37.3 billion, a rise of 13% compared to $33.0 billion in 2020. This was primarily due to strong volume growth as well as improved pricing and mix.</code>         | <code>How did Coca-Cola's revenue performance in 2021 measure against its previous year?</code> |
 * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
   ```json
   {
@@ -677,7 +676,7 @@ You can finetune this model on your own dataset.
 - `per_device_eval_batch_size`: 16
 - `gradient_accumulation_steps`: 16
 - `learning_rate`: 2e-05
-- `num_train_epochs`: 4
+- `num_train_epochs`: 10
 - `lr_scheduler_type`: cosine
 - `warmup_ratio`: 0.1
 - `bf16`: True
@@ -705,7 +704,7 @@ You can finetune this model on your own dataset.
 - `adam_beta2`: 0.999
 - `adam_epsilon`: 1e-08
 - `max_grad_norm`: 1.0
-- `num_train_epochs`: 4
+- `num_train_epochs`: 10
 - `max_steps`: -1
 - `lr_scheduler_type`: cosine
 - `lr_scheduler_kwargs`: {}
@@ -801,12 +800,21 @@ You can finetune this model on your own dataset.
 </details>
 
 ### Training Logs
-| Epoch      | Step  | dim_1024_cosine_map@100 | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
-|:----------:|:-----:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
-| 0.8421     | 1     | 0.9032                  | 0.8846                 | 0.9033                 | 0.9109                 | 0.8695                | 0.9186                 |
-| 1.6842     | 2     | 0.9121                  | 0.8948                 | 0.9174                 | 0.9199                 | 0.8777                | 0.9198                 |
-| 2.5263     | 3     | 0.9281                  | 0.9013                 | 0.9202                 | 0.9281                 | 0.8879                | 0.9204                 |
-| **3.3684** | **4** | **0.9281**              | **0.9016**             | **0.9279**             | **0.9281**             | **0.888**             | **0.9284**             |
+| Epoch      | Step   | Training Loss | dim_1024_cosine_map@100 | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
+|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
+| 0.8649     | 6      | -             | 0.8783                  | 0.8651                 | 0.8713                 | 0.8783                 | 0.8439                | 0.8809                 |
+| 1.4414     | 10     | 0.7682        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 1.8739     | 13     | -             | 0.8918                  | 0.8827                 | 0.8875                 | 0.8918                 | 0.8729                | 0.8933                 |
+| 2.8829     | 20     | 0.1465        | 0.8948                  | 0.8896                 | 0.8928                 | 0.8961                 | 0.8884                | 0.8953                 |
+| 3.8919     | 27     | -             | 0.8930                  | 0.8884                 | 0.8917                 | 0.8959                 | 0.8900                | 0.8945                 |
+| 4.3243     | 30     | 0.0646        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 4.9009     | 34     | -             | 0.8972                  | 0.8883                 | 0.8947                 | 0.8955                 | 0.8925                | 0.8970                 |
+| 5.7658     | 40     | 0.0397        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 5.9099     | 41     | -             | 0.8964                  | 0.8915                 | 0.8953                 | 0.8943                 | 0.8926                | 0.8979                 |
+| 6.9189     | 48     | -             | 0.8994                  | 0.8930                 | 0.8966                 | 0.8955                 | 0.8932                | 0.8974                 |
+| 7.2072     | 50     | 0.0319        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 7.9279     | 55     | -             | 0.8998                  | 0.8945                 | 0.8967                 | 0.8961                 | 0.8943                | 0.8999                 |
+| **8.6486** | **60** | **0.0296**    | **0.8994**              | **0.8957**             | **0.898**              | **0.8959**             | **0.8943**            | **0.8999**             |
 
 * The bold row denotes the saved checkpoint.
 
@@ -815,7 +823,7 @@ You can finetune this model on your own dataset.
 - Sentence Transformers: 3.0.1
 - Transformers: 4.41.2
 - PyTorch: 2.1.2+cu121
-- Accelerate: 0.31.0
+- Accelerate: 0.32.1
 - Datasets: 2.19.1
 - Tokenizers: 0.19.1
 

model.safetensors

@@ -1,3 +1,3 @@
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-oid sha256:2e4009d71caf518182c937a9f2af920ce7e5605e580198931f67d4648a87828b
+oid sha256:456f364d78053599abcadcbb14106385840e50fc49e888bc81171c3f6dd4f655
 size 1340612432