Model save

README.md

@@ -12,62 +12,75 @@ tags:
 - loss:MultipleNegativesRankingLoss
 base_model: BAAI/bge-base-en-v1.5
 widget:
-- source_sentence: How many hours of training and development did Bank of America
-    provide to its employees in 2023?
+- source_sentence: What are the components of Comcast's domestic distribution revenue?
   sentences:
-  - In recent years, several jurisdictions have enhanced their laws and regulations
-    in this area, increased their enforcement activities, and/or increased the level
-    of cross-border coordination and information sharing.
-  - In 2023, Bank of America delivered approximately 6.7 million hours of training
-    and development to its teammates through Bank of America Academy.
-  - Chevron affiliates manage a total of 338 thousand net acres, as detailed in the
-    table for acreage distribution as of December 31, 2023.
-- source_sentence: What are the projected trends for Comcast's residential connectivity
-    revenue in 2023?
+  - Cash used in investing activities was $2.3 billion for fiscal 2023, compared to
+    $2.1 billion for fiscal 2022.
+  - Domestic distribution revenue primarily includes revenue generated from the distribution
+    of our television networks operating predominantly in the United States to traditional
+    and virtual multichannel video providers, and from NBC-affiliated and Telemundo-affiliated
+    local broadcast television stations. Our revenue from distribution agreements
+    is generally based on the number of subscribers receiving the programming on our
+    television networks and a per subscriber fee. Distribution revenue also includes
+    Peacock subscription fees.
+  - In January 2023, Alphabet Inc. announced a reduction of its workforce, consequently
+    recording employee severance and related charges of $2.1 billion for the year.
+- source_sentence: What was the noncash pre-tax impairment charge recorded due to
+    the disposal of Vrio's operations in 2021, and what are the main components contributing
+    to this amount?
   sentences:
-  - In 2023, Switzerland’s Federal Council passed legislation which would implement
-    a federal minimum tax in Switzerland of 15% in 2024.
-  - We believe our residential connectivity revenue will increase as a result of growth
-    in average domestic broadband revenue per customer, as well as increases in domestic
-    wireless and international connectivity revenue.
-  - Approximately 97% of our debt securities were investment-grade quality, with a
-    weighted average credit rating of AA- at the end of 2023.
-- source_sentence: What type of merchandise is included under seasonal and electronics
-    merchandise?
+  - The cash equities rate per contract (per 100 shares) for NYSE increased by 6%,
+    from $0.045 in 2022 to $0.048 in 2023.
+  - In the second quarter of 2021, we classified the Vrio disposal group as held-for-sale
+    and reported the disposal group at fair value less cost to sell, which resulted
+    in a noncash, pre-tax impairment charge of $4,555, including approximately $2,100
+    related to accumulated foreign currency translation adjustments and $2,500 related
+    to property, plant and equipment and intangible assets.
+  - 'SECRET LAIR - our internet-based storefront where MAGIC: THE GATHERING fans can
+    purchase exclusive and limited versions of cards.'
+- source_sentence: What does the Corporate and Other segment include in its composition?
   sentences:
-  - Seasonal and electronics merchandise at the company includes items related to
-    Christmas, Easter, Halloween, and Valentine's Day, along with personal electronics
-    like pre-paid cellular phones and services.
-  - Hewlett Packard Enterprise, with over half of their revenue generated overseas,
-    experiences impact from fluctuations in foreign currency exchange rates. These
-    fluctuations have increased product costs and moderated revenue and earnings growth,
-    particularly in recent periods.
-  - Net investment income grew from $597 million in 2021 to $895 million in 2023,
-    which is a 43.0% increase.
-- source_sentence: What are the types of dialysis available for ESKD patients and
-    how often is hemodialysis typically performed?
+  - The segment consists of unallocated corporate expenses and administrative costs
+    and activities not considered when evaluating segment performance as well as certain
+    assets benefiting more than one segment. In addition, intersegment transactions
+    are eliminated within the Corporate and Other segment.
+  - Net cash provided by (used in) operating activities was recorded at $20,930 million
+    for the reported year.
+  - Forward-Looking Statements Certain statements in this report, other than purely
+    historical information, including estimates, projections, statements relating
+    to our business plans, objectives and expected operating results, and the assumptions
+    upon which those statements are based, are “forward-looking statements” within
+    the meaning of the Private Securities Litigation Reform Act of 1995, Section 27A
+    of the Securities Act of 1933 and Section 21E of the Securities Exchange Act of
+    1934.
+- source_sentence: What was the purchase price for the repurchase of Mobility preferred
+    interests by AT&T in 2023?
   sentences:
-  - Note 16 is important in a Form 10-K for providing detailed information on legal
-    proceedings as 'Commitments and Contingencies.'
-  - Amazon believes that the principal competitive factors in its retail businesses
-    include selection, price, and convenience, including fast and reliable fulfillment.
-  - Dialysis options for ESKD patients include hemodialysis, which is usually performed
-    three times per week, and peritoneal dialysis.
-- source_sentence: How much total cash did The Hershey Company use for share repurchases
-    in 2023 excluding excise tax?
+  - Net revenue increased $1.5 billion, or 19%, to $9.6 billion in 2023 from $8.1
+    billion in 2022. On a constant dollar basis, net revenue increased 20%. Comparable
+    sales increased 13%, or 14% on a constant dollar basis. The increase in net revenue
+    was primarily due to increased Americas net revenue. China Mainland and Rest of
+    World net revenue also increased.
+  - Google Services includes products and services such as ads, Android, Chrome, devices,
+    Google Maps, Google Play, Search, and YouTube. Google Services generates revenues
+    primarily from advertising; fees received for consumer subscription-based products
+    such. as YouTube TV, YouTube Music and Premium, and NFL Sunday Ticket; and the
+    sale of apps and in-app purchases and devices.
+  - In April 2023, we also accepted the December 2022 put option notice from the AT&T
+    pension trust and repurchased the remaining 213 million Mobility preferred interests
+    for a purchase price, including accrued and unpaid distributions, of $5,414.
+- source_sentence: What is the maximum leverage ratio allowed before default under
+    the company's credit facility?
   sentences:
-  - In 2023, The Hershey Company used a total of $267.3 million in cash for share
-    repurchases, excluding any excise tax.
-  - Operating income increased $5.8 billion, or 72.8%, in 2023 compared to 2022. The
-    increase in operating income was primarily driven by the absence of $5.8 billion
-    of opioid litigation charges recorded in 2022 and increases in the Pharmacy &
-    Consumer Wellness segment, primarily driven by the absence of a $2.5 billion loss
-    on assets held for sale recorded in 2022 related to the write-down of the Company’s
-    Omnicare® long-term care business which was partially offset by continued pharmacy
-    reimbursement pressure and decreased COVID-19 vaccinations and diagnostic testing
-    compared to 2022, as well as an increase in the Health Services segment.
-  - Net income for the year ended December 31, 2023, was $307,568, contrasting with
-    a net loss of $694,288 in 2022.
+  - If the company's leverage ratio exceeds 3.50 to 1, it would be in default of its
+    revolving credit facility, impairing its ability to borrow under the facility.
+  - Research and Development Because the industries in which the Company competes
+    are characterized by rapid technological advances, the Company’s ability to compete
+    successfully depends heavily upon its ability to ensure a continual and timely
+    flow of competitive products, services and technologies to the marketplace.
+  - Visa is focused on extending, enhancing and investing in VisaNet, their proprietary
+    advanced transaction processing network, to offer a single connection point for
+    facilitating payment transactions to multiple endpoints through various form factors.
 datasets:
 - philschmid/finanical-rag-embedding-dataset
 pipeline_tag: sentence-similarity
@@ -99,49 +112,49 @@ model-index:
       type: dim_768
     metrics:
     - type: cosine_accuracy@1
-      value: 0.6914285714285714
+      value: 0.6771428571428572
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.8257142857142857
+      value: 0.8371428571428572
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
       value: 0.8685714285714285
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.9228571428571428
+      value: 0.9185714285714286
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.6914285714285714
+      value: 0.6771428571428572
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.2752380952380952
+      value: 0.27904761904761904
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.1737142857142857
+      value: 0.17371428571428568
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.09228571428571428
+      value: 0.09185714285714283
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.6914285714285714
+      value: 0.6771428571428572
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.8257142857142857
+      value: 0.8371428571428572
       name: Cosine Recall@3
     - type: cosine_recall@5
       value: 0.8685714285714285
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.9228571428571428
+      value: 0.9185714285714286
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.8071406101424283
+      value: 0.800782444183487
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.770200113378685
+      value: 0.762721088435374
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.7731689567146356
+      value: 0.7655884035994069
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -151,49 +164,49 @@ model-index:
       type: dim_512
     metrics:
     - type: cosine_accuracy@1
-      value: 0.6985714285714286
+      value: 0.6828571428571428
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.8314285714285714
+      value: 0.8371428571428572
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.8685714285714285
+      value: 0.8757142857142857
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.9142857142857143
+      value: 0.92
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.6985714285714286
+      value: 0.6828571428571428
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.27714285714285714
+      value: 0.27904761904761904
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.17371428571428568
+      value: 0.17514285714285713
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.09142857142857141
+      value: 0.09199999999999998
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.6985714285714286
+      value: 0.6828571428571428
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.8314285714285714
+      value: 0.8371428571428572
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.8685714285714285
+      value: 0.8757142857142857
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.9142857142857143
+      value: 0.92
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.8065430842560983
+      value: 0.80444342170685
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.7719557823129252
+      value: 0.7670583900226756
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.775512801809706
+      value: 0.7699510134898729
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -203,49 +216,49 @@ model-index:
       type: dim_256
     metrics:
     - type: cosine_accuracy@1
-      value: 0.6842857142857143
+      value: 0.6757142857142857
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.8214285714285714
+      value: 0.8228571428571428
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.8671428571428571
+      value: 0.8642857142857143
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.9057142857142857
+      value: 0.9185714285714286
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.6842857142857143
+      value: 0.6757142857142857
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.2738095238095238
+      value: 0.2742857142857143
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.1734285714285714
+      value: 0.17285714285714285
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.09057142857142855
+      value: 0.09185714285714283
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.6842857142857143
+      value: 0.6757142857142857
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.8214285714285714
+      value: 0.8228571428571428
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.8671428571428571
+      value: 0.8642857142857143
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.9057142857142857
+      value: 0.9185714285714286
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.7965883498968402
+      value: 0.7984105242762846
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.7613792517006803
+      value: 0.7599024943310656
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.7655926405987631
+      value: 0.7625291382895937
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -255,49 +268,49 @@ model-index:
       type: dim_128
     metrics:
     - type: cosine_accuracy@1
-      value: 0.6828571428571428
+      value: 0.6714285714285714
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.8157142857142857
+      value: 0.8114285714285714
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.8557142857142858
+      value: 0.8485714285714285
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.9057142857142857
+      value: 0.9014285714285715
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.6828571428571428
+      value: 0.6714285714285714
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.27190476190476187
+      value: 0.2704761904761904
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.17114285714285712
+      value: 0.16971428571428568
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.09057142857142855
+      value: 0.09014285714285714
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.6828571428571428
+      value: 0.6714285714285714
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.8157142857142857
+      value: 0.8114285714285714
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.8557142857142858
+      value: 0.8485714285714285
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.9057142857142857
+      value: 0.9014285714285715
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.7942960704612301
+      value: 0.7872870842648211
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.7586780045351473
+      value: 0.7507193877551018
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.7624961899058385
+      value: 0.7542921487122674
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -307,49 +320,49 @@ model-index:
       type: dim_64
     metrics:
     - type: cosine_accuracy@1
-      value: 0.6485714285714286
+      value: 0.6242857142857143
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.7771428571428571
+      value: 0.7842857142857143
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.8171428571428572
+      value: 0.82
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.87
+      value: 0.8828571428571429
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.6485714285714286
+      value: 0.6242857142857143
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.2590476190476191
+      value: 0.26142857142857145
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.16342857142857142
+      value: 0.16399999999999998
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.087
+      value: 0.08828571428571429
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.6485714285714286
+      value: 0.6242857142857143
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.7771428571428571
+      value: 0.7842857142857143
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.8171428571428572
+      value: 0.82
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.87
+      value: 0.8828571428571429
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.7582844308652432
+      value: 0.7546358861091382
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.7225646258503399
+      value: 0.7135277777777775
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.7276362979042951
+      value: 0.7174129354945035
       name: Cosine Map@100
 ---
 
@@ -404,9 +417,9 @@ from sentence_transformers import SentenceTransformer
 model = SentenceTransformer("bnkc123/bge-base-financial-matryoshka")
 # Run inference
 sentences = [
-    'How much total cash did The Hershey Company use for share repurchases in 2023 excluding excise tax?',
-    'In 2023, The Hershey Company used a total of $267.3 million in cash for share repurchases, excluding any excise tax.',
-    'Operating income increased $5.8 billion, or 72.8%, in 2023 compared to 2022. The increase in operating income was primarily driven by the absence of $5.8 billion of opioid litigation charges recorded in 2022 and increases in the Pharmacy & Consumer Wellness segment, primarily driven by the absence of a $2.5 billion loss on assets held for sale recorded in 2022 related to the write-down of the Company’s Omnicare® long-term care business which was partially offset by continued pharmacy reimbursement pressure and decreased COVID-19 vaccinations and diagnostic testing compared to 2022, as well as an increase in the Health Services segment.',
+    "What is the maximum leverage ratio allowed before default under the company's credit facility?",
+    "If the company's leverage ratio exceeds 3.50 to 1, it would be in default of its revolving credit facility, impairing its ability to borrow under the facility.",
+    'Research and Development Because the industries in which the Company competes are characterized by rapid technological advances, the Company’s ability to compete successfully depends heavily upon its ability to ensure a continual and timely flow of competitive products, services and technologies to the marketplace.',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -458,21 +471,21 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| cosine_accuracy@1   | 0.6914     |
-| cosine_accuracy@3   | 0.8257     |
+| cosine_accuracy@1   | 0.6771     |
+| cosine_accuracy@3   | 0.8371     |
 | cosine_accuracy@5   | 0.8686     |
-| cosine_accuracy@10  | 0.9229     |
-| cosine_precision@1  | 0.6914     |
-| cosine_precision@3  | 0.2752     |
+| cosine_accuracy@10  | 0.9186     |
+| cosine_precision@1  | 0.6771     |
+| cosine_precision@3  | 0.279      |
 | cosine_precision@5  | 0.1737     |
-| cosine_precision@10 | 0.0923     |
-| cosine_recall@1     | 0.6914     |
-| cosine_recall@3     | 0.8257     |
+| cosine_precision@10 | 0.0919     |
+| cosine_recall@1     | 0.6771     |
+| cosine_recall@3     | 0.8371     |
 | cosine_recall@5     | 0.8686     |
-| cosine_recall@10    | 0.9229     |
-| **cosine_ndcg@10**  | **0.8071** |
-| cosine_mrr@10       | 0.7702     |
-| cosine_map@100      | 0.7732     |
+| cosine_recall@10    | 0.9186     |
+| **cosine_ndcg@10**  | **0.8008** |
+| cosine_mrr@10       | 0.7627     |
+| cosine_map@100      | 0.7656     |
 
 #### Information Retrieval
 
@@ -486,21 +499,21 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| cosine_accuracy@1   | 0.6986     |
-| cosine_accuracy@3   | 0.8314     |
-| cosine_accuracy@5   | 0.8686     |
-| cosine_accuracy@10  | 0.9143     |
-| cosine_precision@1  | 0.6986     |
-| cosine_precision@3  | 0.2771     |
-| cosine_precision@5  | 0.1737     |
-| cosine_precision@10 | 0.0914     |
-| cosine_recall@1     | 0.6986     |
-| cosine_recall@3     | 0.8314     |
-| cosine_recall@5     | 0.8686     |
-| cosine_recall@10    | 0.9143     |
-| **cosine_ndcg@10**  | **0.8065** |
-| cosine_mrr@10       | 0.772      |
-| cosine_map@100      | 0.7755     |
+| cosine_accuracy@1   | 0.6829     |
+| cosine_accuracy@3   | 0.8371     |
+| cosine_accuracy@5   | 0.8757     |
+| cosine_accuracy@10  | 0.92       |
+| cosine_precision@1  | 0.6829     |
+| cosine_precision@3  | 0.279      |
+| cosine_precision@5  | 0.1751     |
+| cosine_precision@10 | 0.092      |
+| cosine_recall@1     | 0.6829     |
+| cosine_recall@3     | 0.8371     |
+| cosine_recall@5     | 0.8757     |
+| cosine_recall@10    | 0.92       |
+| **cosine_ndcg@10**  | **0.8044** |
+| cosine_mrr@10       | 0.7671     |
+| cosine_map@100      | 0.77       |
 
 #### Information Retrieval
 
@@ -514,21 +527,21 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| cosine_accuracy@1   | 0.6843     |
-| cosine_accuracy@3   | 0.8214     |
-| cosine_accuracy@5   | 0.8671     |
-| cosine_accuracy@10  | 0.9057     |
-| cosine_precision@1  | 0.6843     |
-| cosine_precision@3  | 0.2738     |
-| cosine_precision@5  | 0.1734     |
-| cosine_precision@10 | 0.0906     |
-| cosine_recall@1     | 0.6843     |
-| cosine_recall@3     | 0.8214     |
-| cosine_recall@5     | 0.8671     |
-| cosine_recall@10    | 0.9057     |
-| **cosine_ndcg@10**  | **0.7966** |
-| cosine_mrr@10       | 0.7614     |
-| cosine_map@100      | 0.7656     |
+| cosine_accuracy@1   | 0.6757     |
+| cosine_accuracy@3   | 0.8229     |
+| cosine_accuracy@5   | 0.8643     |
+| cosine_accuracy@10  | 0.9186     |
+| cosine_precision@1  | 0.6757     |
+| cosine_precision@3  | 0.2743     |
+| cosine_precision@5  | 0.1729     |
+| cosine_precision@10 | 0.0919     |
+| cosine_recall@1     | 0.6757     |
+| cosine_recall@3     | 0.8229     |
+| cosine_recall@5     | 0.8643     |
+| cosine_recall@10    | 0.9186     |
+| **cosine_ndcg@10**  | **0.7984** |
+| cosine_mrr@10       | 0.7599     |
+| cosine_map@100      | 0.7625     |
 
 #### Information Retrieval
 
@@ -542,21 +555,21 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| cosine_accuracy@1   | 0.6829     |
-| cosine_accuracy@3   | 0.8157     |
-| cosine_accuracy@5   | 0.8557     |
-| cosine_accuracy@10  | 0.9057     |
-| cosine_precision@1  | 0.6829     |
-| cosine_precision@3  | 0.2719     |
-| cosine_precision@5  | 0.1711     |
-| cosine_precision@10 | 0.0906     |
-| cosine_recall@1     | 0.6829     |
-| cosine_recall@3     | 0.8157     |
-| cosine_recall@5     | 0.8557     |
-| cosine_recall@10    | 0.9057     |
-| **cosine_ndcg@10**  | **0.7943** |
-| cosine_mrr@10       | 0.7587     |
-| cosine_map@100      | 0.7625     |
+| cosine_accuracy@1   | 0.6714     |
+| cosine_accuracy@3   | 0.8114     |
+| cosine_accuracy@5   | 0.8486     |
+| cosine_accuracy@10  | 0.9014     |
+| cosine_precision@1  | 0.6714     |
+| cosine_precision@3  | 0.2705     |
+| cosine_precision@5  | 0.1697     |
+| cosine_precision@10 | 0.0901     |
+| cosine_recall@1     | 0.6714     |
+| cosine_recall@3     | 0.8114     |
+| cosine_recall@5     | 0.8486     |
+| cosine_recall@10    | 0.9014     |
+| **cosine_ndcg@10**  | **0.7873** |
+| cosine_mrr@10       | 0.7507     |
+| cosine_map@100      | 0.7543     |
 
 #### Information Retrieval
 
@@ -570,21 +583,21 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| cosine_accuracy@1   | 0.6486     |
-| cosine_accuracy@3   | 0.7771     |
-| cosine_accuracy@5   | 0.8171     |
-| cosine_accuracy@10  | 0.87       |
-| cosine_precision@1  | 0.6486     |
-| cosine_precision@3  | 0.259      |
-| cosine_precision@5  | 0.1634     |
-| cosine_precision@10 | 0.087      |
-| cosine_recall@1     | 0.6486     |
-| cosine_recall@3     | 0.7771     |
-| cosine_recall@5     | 0.8171     |
-| cosine_recall@10    | 0.87       |
-| **cosine_ndcg@10**  | **0.7583** |
-| cosine_mrr@10       | 0.7226     |
-| cosine_map@100      | 0.7276     |
+| cosine_accuracy@1   | 0.6243     |
+| cosine_accuracy@3   | 0.7843     |
+| cosine_accuracy@5   | 0.82       |
+| cosine_accuracy@10  | 0.8829     |
+| cosine_precision@1  | 0.6243     |
+| cosine_precision@3  | 0.2614     |
+| cosine_precision@5  | 0.164      |
+| cosine_precision@10 | 0.0883     |
+| cosine_recall@1     | 0.6243     |
+| cosine_recall@3     | 0.7843     |
+| cosine_recall@5     | 0.82       |
+| cosine_recall@10    | 0.8829     |
+| **cosine_ndcg@10**  | **0.7546** |
+| cosine_mrr@10       | 0.7135     |
+| cosine_map@100      | 0.7174     |
 
 <!--
 ## Bias, Risks and Limitations
@@ -608,16 +621,16 @@ You can finetune this model on your own dataset.
 * Size: 6,300 training samples
 * Columns: <code>anchor</code> and <code>positive</code>
 * Approximate statistics based on the first 1000 samples:
-  |         | anchor                                                                            | positive                                                                           |
-  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
-  | type    | string                                                                            | string                                                                             |
-  | details | <ul><li>min: 9 tokens</li><li>mean: 20.65 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 45.4 tokens</li><li>max: 512 tokens</li></ul> |
+  |         | anchor                                                                           | positive                                                                           |
+  |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                             |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 20.5 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 46.09 tokens</li><li>max: 512 tokens</li></ul> |
 * Samples:
-  | anchor                                                                                                                                                            | positive                                                                                                                                                                                                                                                                       |
-  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-  | <code>How much cash did FedEx have at the end of May 2023?</code>                                                                                                 | <code>FedEx reported having $6.9 billion in cash and cash equivalents at the end of May 2023.</code>                                                                                                                                                                           |
-  | <code>What were Caterpillar's total obligations for the purchase of goods and services as of December 31, 2023?</code>                                            | <code>We have short-term obligations related to the purchase of goods and services made in the ordinary course of business. These consist of invoices received and recorded as liabilities as of December 31, 2023, but scheduled for payment in 2024 of $7.91 billion.</code> |
-  | <code>What was the total number of outstanding stock option awards at the beginning and end of 2023, and what were their weighted average exercise prices?</code> | <code>Stock option activity under the Plan for the years ended reveals that stock options both started and ended with 6.2 million outstanding in 2023. The weighted average exercise price at the beginning of the year was $50.40 and $50.42 at the end.</code>               |
+  | anchor                                                                                                                                                    | positive                                                                                                                                                                                                                                                                                                                 |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What was the amount of premiums written by Berkshire Hathaway's Insurance Underwriting in 2023, and how did it compare to the previous year?</code> | <code>Premiums written increased $3.5 billion (24.1%) in 2023 compared to 2022. The increase was primarily due to RSUI and CapSpecialty ($2.1 billion), as well as comparative increases from BHSI and BH Direct, and to a lesser extent the other businesses. Premiums written | $ | 18,142 | | | | $ | 14,619 |</code> |
+  | <code>What types of transportation equipment does XTRA Corporation manage in its fleet?</code>                                                            | <code>XTRA manages a diverse fleet of approximately 90,000 units located at 47 facilities throughout the U.S. The fleet includes over-the-road and storage trailers, chassis, temperature-controlled vans and flatbed trailers.</code>                                                                                   |
+  | <code>What seasonal trends affect the company's sales volumes?</code>                                                                                     | <code>Sales volumes for the company are highest in the second fiscal quarter due to seasonal influences, particularly during the spring season in the regions it serves.</code>                                                                                                                                          |
 * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
   ```json
   {
@@ -782,14 +795,14 @@ You can finetune this model on your own dataset.
 ### 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 |
 |:---------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
-| 0.8122    | 10     | 25.3869       | -                      | -                      | -                      | -                      | -                     |
-| 1.0       | 13     | -             | 0.7943                 | 0.7907                 | 0.7884                 | 0.7756                 | 0.7419                |
-| 1.5685    | 20     | 9.8731        | -                      | -                      | -                      | -                      | -                     |
-| 2.0       | 26     | -             | 0.8040                 | 0.8032                 | 0.7939                 | 0.7906                 | 0.7553                |
-| 2.3249    | 30     | 7.6627        | -                      | -                      | -                      | -                      | -                     |
-| 3.0       | 39     | -             | 0.8067                 | 0.8054                 | 0.7989                 | 0.7930                 | 0.7584                |
-| 3.0812    | 40     | 6.5397        | -                      | -                      | -                      | -                      | -                     |
-| **3.731** | **48** | **-**         | **0.8071**             | **0.8065**             | **0.7966**             | **0.7943**             | **0.7583**            |
+| 0.8122    | 10     | 25.483        | -                      | -                      | -                      | -                      | -                     |
+| 1.0       | 13     | -             | 0.7890                 | 0.7887                 | 0.7815                 | 0.7647                 | 0.7280                |
+| 1.5685    | 20     | 9.1323        | -                      | -                      | -                      | -                      | -                     |
+| 2.0       | 26     | -             | 0.7952                 | 0.7982                 | 0.7933                 | 0.7801                 | 0.7477                |
+| 2.3249    | 30     | 6.7535        | -                      | -                      | -                      | -                      | -                     |
+| 3.0       | 39     | -             | 0.8019                 | 0.8048                 | 0.7989                 | 0.7865                 | 0.7547                |
+| 3.0812    | 40     | 6.5646        | -                      | -                      | -                      | -                      | -                     |
+| **3.731** | **48** | **-**         | **0.8008**             | **0.8044**             | **0.7984**             | **0.7873**             | **0.7546**            |
 
 * The bold row denotes the saved checkpoint.