Model save

1_Pooling/config.json

@@ -1,7 +1,7 @@
 {
   "word_embedding_dimension": 768,
-  "pooling_mode_cls_token": true,
-  "pooling_mode_mean_tokens": false,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
   "pooling_mode_max_tokens": false,
   "pooling_mode_mean_sqrt_len_tokens": false,
   "pooling_mode_weightedmean_tokens": false,

README.md

@@ -10,16 +10,15 @@ tags:
 - dataset_size:150
 - loss:MatryoshkaLoss
 - loss:MultipleNegativesRankingLoss
-base_model: BAAI/bge-base-en-v1.5
+base_model: sentence-transformers/msmarco-distilbert-base-v4
 widget:
 - source_sentence: What services does Techchefz Digital offer for AI adoption?
   sentences:
-  - 'We are a New breed of innovative digital transformation agency, redefining storytelling
-    for an always-on world.
+  - 'How can we get started with your DevOps solutions?
 
-    With roots dating back to 2017, we started as a pocket size team of enthusiasts
-    with a goal of helping traditional businesses transform and create dynamic, digital
-    cultures through disruptive strategies and agile deployment of innovative solutions.'
+    Getting started is easy. Contact us through our website. We''ll schedule a consultation
+    to discuss your needs, evaluate your current infrastructure, and propose a customized
+    DevOps solution designed to achieve your goals.'
   - "At Techchefz Digital, we specialize in guiding companies through the complexities\
     \ of adopting and integrating Artificial Intelligence and Machine Learning technologies.\
     \ Our consultancy services are designed to enhance your operational efficiency\
@@ -29,12 +28,13 @@ widget:
     \                                                                            \
     \                                           \n DATA INTELLIGENCE PLATFORMS we\
     \ specialize in\nTensorFlow\nDatabricks\nTableau\nPytorch\nOpenAI\nPinecone\""
-  - 'How can we get started with your DevOps solutions?
+  - 'We are a New breed of innovative digital transformation agency, redefining storytelling
+    for an always-on world.
 
-    Getting started is easy. Contact us through our website. We''ll schedule a consultation
-    to discuss your needs, evaluate your current infrastructure, and propose a customized
-    DevOps solution designed to achieve your goals.'
-- source_sentence: Hav you made any services for schools and students?
+    With roots dating back to 2017, we started as a pocket size team of enthusiasts
+    with a goal of helping traditional businesses transform and create dynamic, digital
+    cultures through disruptive strategies and agile deployment of innovative solutions.'
+- source_sentence: Do you provide support 24/7?
   sentences:
   - 'How do we do Custom Development ?
 
@@ -105,7 +105,7 @@ widget:
     \ operations.\nTeam Augmentation\nHelp you scale up and augment your existing\
     \ team to solve your hiring challenges with our easy to deploy staff augmentation\
     \ offerings.\""
-- source_sentence: How did TechChefz evolve from its early days?
+- source_sentence: What challenges did the company face in its early days?
   sentences:
   - 'Why do we need Microservices ?
 
@@ -145,22 +145,14 @@ widget:
     and exponential growth. His leadership has been instrumental in shaping TechChefz
     Digital into a leading force in the digital transformation arena, inspiring a
     culture of innovation and excellence that continues to propel the company forward.'
-  - 'In what ways can machine learning optimize our operations?
+  - 'What makes your DevOps solutions stand out from the competition?
 
-    Machine learning algorithms can analyze operational data to identify inefficiencies,
-    predict maintenance needs, optimize supply chains, and automate repetitive tasks,
-    significantly improving operational efficiency and reducing costs.'
+    Our DevOps solutions stand out due to our personalized approach, extensive expertise,
+    and commitment to innovation. We focus on delivering measurable results, such
+    as reduced deployment times, improved system reliability, and enhanced security,
+    ensuring you get the maximum benefit from our services.'
 - source_sentence: What kind of data do you leverage for AI solutions?
   sentences:
-  - 'In the Introducing the world of Global Insurance Firm, we crafted Effective Solutions
-    for Complex Problems and delieverd a comprehensive Website Development, Production
-    Support & Managed Services, we optimized customer journeys, integrate analytics,
-    CRM, ERP, and third-party applications, and implement cutting-edge technologies
-    for enhanced performance and efficiency
-
-    and achievied 200% Reduction in operational time & effort managing content & experience,
-    70% Reduction in Deployment Errors and Downtime, 2.5X Customer Engagement, Conversion
-    & Retention'
   - 'Our Solutions
 
     Strategy & Digital Transformation
@@ -175,6 +167,11 @@ widget:
     Providing product development, enterprise web and mobile development, microservices
     integrations, quality engineering, and application support services to drive innovation
     and enhance operational efficiency.'
+  - 'In what ways can machine learning optimize our operations?
+
+    Machine learning algorithms can analyze operational data to identify inefficiencies,
+    predict maintenance needs, optimize supply chains, and automate repetitive tasks,
+    significantly improving operational efficiency and reducing costs.'
   - Our AI/ML services pave the way for transformative change across industries, embodying
     a client-focused approach that integrates seamlessly with human-centric innovation.
     Our collaborative teams are dedicated to fostering growth, leveraging data, and
@@ -193,12 +190,6 @@ widget:
     \ deploy staff augmentation offerings .\nManaged Services\nOperate and monitor\
     \ your business-critical applications, data, and IT workloads, along with Application\
     \ maintenance and operations\n"
-  - 'What makes your DevOps solutions stand out from the competition?
-
-    Our DevOps solutions stand out due to our personalized approach, extensive expertise,
-    and commitment to innovation. We focus on delivering measurable results, such
-    as reduced deployment times, improved system reliability, and enhanced security,
-    ensuring you get the maximum benefit from our services.'
   - 'Introducing the world of General Insurance Firm
 
     In this project, we implemented Digital Solution and Implementation with Headless
@@ -222,6 +213,15 @@ widget:
     & Experience for Buy & renew Journeys,220% Reduction in Customer Drops during
     buy and renewal journeys, 300% Reduction in bounce rate on policy landing and
     campaign pages'
+  - 'In the Introducing the world of Global Insurance Firm, we crafted Effective Solutions
+    for Complex Problems and delieverd a comprehensive Website Development, Production
+    Support & Managed Services, we optimized customer journeys, integrate analytics,
+    CRM, ERP, and third-party applications, and implement cutting-edge technologies
+    for enhanced performance and efficiency
+
+    and achievied 200% Reduction in operational time & effort managing content & experience,
+    70% Reduction in Deployment Errors and Downtime, 2.5X Customer Engagement, Conversion
+    & Retention'
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
 metrics:
@@ -251,49 +251,49 @@ model-index:
       type: dim_768
     metrics:
     - type: cosine_accuracy@1
-      value: 0.17333333333333334
+      value: 0.10666666666666667
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.5466666666666666
+      value: 0.49333333333333335
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.6
+      value: 0.5333333333333333
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.6933333333333334
+      value: 0.6266666666666667
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.17333333333333334
+      value: 0.10666666666666667
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.1822222222222222
+      value: 0.16444444444444445
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.12
+      value: 0.10666666666666667
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.06933333333333333
+      value: 0.06266666666666666
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.17333333333333334
+      value: 0.10666666666666667
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.5466666666666666
+      value: 0.49333333333333335
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.6
+      value: 0.5333333333333333
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.6933333333333334
+      value: 0.6266666666666667
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.43705488094312567
+      value: 0.3696947495406473
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.3539576719576719
+      value: 0.2864550264550264
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.3663753684578632
+      value: 0.2993424751990436
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -303,49 +303,49 @@ model-index:
       type: dim_512
     metrics:
     - type: cosine_accuracy@1
-      value: 0.17333333333333334
+      value: 0.10666666666666667
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.5333333333333333
+      value: 0.4666666666666667
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.6266666666666667
+      value: 0.5333333333333333
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.6933333333333334
+      value: 0.6133333333333333
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.17333333333333334
+      value: 0.10666666666666667
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.17777777777777776
+      value: 0.15555555555555556
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.12533333333333332
+      value: 0.10666666666666667
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.06933333333333333
+      value: 0.06133333333333333
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.17333333333333334
+      value: 0.10666666666666667
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.5333333333333333
+      value: 0.4666666666666667
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.6266666666666667
+      value: 0.5333333333333333
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.6933333333333334
+      value: 0.6133333333333333
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.43324477959330543
+      value: 0.3702942720383175
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.3495185185185184
+      value: 0.29092063492063486
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.359896266319179
+      value: 0.3047495006876888
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -355,49 +355,49 @@ model-index:
       type: dim_256
     metrics:
     - type: cosine_accuracy@1
-      value: 0.22666666666666666
+      value: 0.14666666666666667
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.49333333333333335
+      value: 0.4533333333333333
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.56
+      value: 0.49333333333333335
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.68
+      value: 0.6
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.22666666666666666
+      value: 0.14666666666666667
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.16444444444444445
+      value: 0.1511111111111111
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.11199999999999997
+      value: 0.09866666666666667
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.06799999999999998
+      value: 0.06
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.22666666666666666
+      value: 0.14666666666666667
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.49333333333333335
+      value: 0.4533333333333333
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.56
+      value: 0.49333333333333335
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.68
+      value: 0.6
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.4383628839300849
+      value: 0.37318151343456746
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.36210582010582004
+      value: 0.3006455026455026
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.3731640827722892
+      value: 0.31352550381063704
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -407,49 +407,49 @@ model-index:
       type: dim_128
     metrics:
     - type: cosine_accuracy@1
-      value: 0.24
+      value: 0.12
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.48
+      value: 0.4533333333333333
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.56
+      value: 0.49333333333333335
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.6933333333333334
+      value: 0.6
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.24
+      value: 0.12
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.16
+      value: 0.1511111111111111
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.11199999999999997
+      value: 0.09866666666666667
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.06933333333333332
+      value: 0.06
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.24
+      value: 0.12
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.48
+      value: 0.4533333333333333
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.56
+      value: 0.49333333333333335
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.6933333333333334
+      value: 0.6
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.4443870388298522
+      value: 0.349467831727335
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.36651322751322746
+      value: 0.26956613756613756
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.37546675549059694
+      value: 0.2814743968696581
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -459,61 +459,61 @@ model-index:
       type: dim_64
     metrics:
     - type: cosine_accuracy@1
-      value: 0.08
+      value: 0.16
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.3466666666666667
+      value: 0.38666666666666666
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.49333333333333335
+      value: 0.4666666666666667
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.56
+      value: 0.5466666666666666
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.08
+      value: 0.16
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.11555555555555555
+      value: 0.1288888888888889
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.09866666666666667
+      value: 0.09333333333333335
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.05599999999999999
+      value: 0.05466666666666666
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.08
+      value: 0.16
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.3466666666666667
+      value: 0.38666666666666666
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.49333333333333335
+      value: 0.4666666666666667
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.56
+      value: 0.5466666666666666
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.3120295466486537
+      value: 0.34485137335598726
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.23260846560846554
+      value: 0.28099999999999997
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.24731947636993173
+      value: 0.29532589563098727
       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). 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.
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/msmarco-distilbert-base-v4](https://huggingface.co/sentence-transformers/msmarco-distilbert-base-v4). 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.
 
 ## 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 -->
+- **Base model:** [sentence-transformers/msmarco-distilbert-base-v4](https://huggingface.co/sentence-transformers/msmarco-distilbert-base-v4) <!-- at revision 19f0f4c73dc418bad0e0fc600611e808b7448a28 -->
 - **Maximum Sequence Length:** 512 tokens
 - **Output Dimensionality:** 768 dimensions
 - **Similarity Function:** Cosine Similarity
@@ -531,9 +531,8 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [B
 
 ```
 SentenceTransformer(
-  (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()
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
 )
 ```
 
@@ -552,12 +551,12 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("Shashwat13333/bge-base-en-v1.5")
+model = SentenceTransformer("Shashwat13333/msmarco-distilbert-base-v4")
 # Run inference
 sentences = [
     'What managed services does TechChefz provide ?',
     '  What we do\n\nDigital Strategy\nCreating digital frameworks that transform your digital enterprise and produce a return on investment.\n\nPlatform Selection\nHelping you select the optimal digital experience, commerce, cloud and marketing platform for your enterprise.\n\nPlatform Builds\nDeploying next-gen scalable and agile enterprise digital platforms, along with multi-platform integrations.\n\nProduct Builds\nHelp you ideate, strategize, and engineer your product with help of our enterprise frameworks \n\nTeam Augmentation\nHelp you scale up and augment your existing team to solve your hiring challenges with our easy to deploy staff augmentation offerings .\nManaged Services\nOperate and monitor your business-critical applications, data, and IT workloads, along with Application maintenance and operations\n',
-    'Introducing the world of General Insurance Firm\nIn this project, we implemented Digital Solution and Implementation with Headless Drupal as the CMS, and lightweight React JS (Next JS SSR on Node JS) with the following features:\nPWA & AMP based Web Pages\nPage Speed Optimization\nReusable and scalable React JS / Next JS Templates and Components\nHeadless Drupal CMS with Content & Experience management, approval workflows, etc for seamless collaboration between the business and marketing teams\nMinimalistic Buy and Renewal Journeys for various products, with API integrations and adherence to data compliances\n\nWe achieved 250% Reduction in Operational Time and Effort in managing the Content & Experience for Buy & renew Journeys,220% Reduction in Customer Drops during buy and renewal journeys, 300% Reduction in bounce rate on policy landing and campaign pages',
+    'In the Introducing the world of Global Insurance Firm, we crafted Effective Solutions for Complex Problems and delieverd a comprehensive Website Development, Production Support & Managed Services, we optimized customer journeys, integrate analytics, CRM, ERP, and third-party applications, and implement cutting-edge technologies for enhanced performance and efficiency\nand achievied 200% Reduction in operational time & effort managing content & experience, 70% Reduction in Deployment Errors and Downtime, 2.5X Customer Engagement, Conversion & Retention',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -602,23 +601,23 @@ You can finetune this model on your own dataset.
 * Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
 * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
 
-| Metric              | dim_768    | dim_512    | dim_256    | dim_128    | dim_64    |
-|:--------------------|:-----------|:-----------|:-----------|:-----------|:----------|
-| cosine_accuracy@1   | 0.1733     | 0.1733     | 0.2267     | 0.24       | 0.08      |
-| cosine_accuracy@3   | 0.5467     | 0.5333     | 0.4933     | 0.48       | 0.3467    |
-| cosine_accuracy@5   | 0.6        | 0.6267     | 0.56       | 0.56       | 0.4933    |
-| cosine_accuracy@10  | 0.6933     | 0.6933     | 0.68       | 0.6933     | 0.56      |
-| cosine_precision@1  | 0.1733     | 0.1733     | 0.2267     | 0.24       | 0.08      |
-| cosine_precision@3  | 0.1822     | 0.1778     | 0.1644     | 0.16       | 0.1156    |
-| cosine_precision@5  | 0.12       | 0.1253     | 0.112      | 0.112      | 0.0987    |
-| cosine_precision@10 | 0.0693     | 0.0693     | 0.068      | 0.0693     | 0.056     |
-| cosine_recall@1     | 0.1733     | 0.1733     | 0.2267     | 0.24       | 0.08      |
-| cosine_recall@3     | 0.5467     | 0.5333     | 0.4933     | 0.48       | 0.3467    |
-| cosine_recall@5     | 0.6        | 0.6267     | 0.56       | 0.56       | 0.4933    |
-| cosine_recall@10    | 0.6933     | 0.6933     | 0.68       | 0.6933     | 0.56      |
-| **cosine_ndcg@10**  | **0.4371** | **0.4332** | **0.4384** | **0.4444** | **0.312** |
-| cosine_mrr@10       | 0.354      | 0.3495     | 0.3621     | 0.3665     | 0.2326    |
-| cosine_map@100      | 0.3664     | 0.3599     | 0.3732     | 0.3755     | 0.2473    |
+| Metric              | dim_768    | dim_512    | dim_256    | dim_128    | dim_64     |
+|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|
+| cosine_accuracy@1   | 0.1067     | 0.1067     | 0.1467     | 0.12       | 0.16       |
+| cosine_accuracy@3   | 0.4933     | 0.4667     | 0.4533     | 0.4533     | 0.3867     |
+| cosine_accuracy@5   | 0.5333     | 0.5333     | 0.4933     | 0.4933     | 0.4667     |
+| cosine_accuracy@10  | 0.6267     | 0.6133     | 0.6        | 0.6        | 0.5467     |
+| cosine_precision@1  | 0.1067     | 0.1067     | 0.1467     | 0.12       | 0.16       |
+| cosine_precision@3  | 0.1644     | 0.1556     | 0.1511     | 0.1511     | 0.1289     |
+| cosine_precision@5  | 0.1067     | 0.1067     | 0.0987     | 0.0987     | 0.0933     |
+| cosine_precision@10 | 0.0627     | 0.0613     | 0.06       | 0.06       | 0.0547     |
+| cosine_recall@1     | 0.1067     | 0.1067     | 0.1467     | 0.12       | 0.16       |
+| cosine_recall@3     | 0.4933     | 0.4667     | 0.4533     | 0.4533     | 0.3867     |
+| cosine_recall@5     | 0.5333     | 0.5333     | 0.4933     | 0.4933     | 0.4667     |
+| cosine_recall@10    | 0.6267     | 0.6133     | 0.6        | 0.6        | 0.5467     |
+| **cosine_ndcg@10**  | **0.3697** | **0.3703** | **0.3732** | **0.3495** | **0.3449** |
+| cosine_mrr@10       | 0.2865     | 0.2909     | 0.3006     | 0.2696     | 0.281      |
+| cosine_map@100      | 0.2993     | 0.3047     | 0.3135     | 0.2815     | 0.2953     |
 
 <!--
 ## Bias, Risks and Limitations
@@ -642,16 +641,16 @@ You can finetune this model on your own dataset.
 * Size: 150 training samples
 * Columns: <code>anchor</code> and <code>positive</code>
 * Approximate statistics based on the first 150 samples:
-  |         | anchor                                                                           | positive                                                                             |
-  |:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
-  | type    | string                                                                           | string                                                                               |
-  | details | <ul><li>min: 7 tokens</li><li>mean: 12.4 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 126.17 tokens</li><li>max: 378 tokens</li></ul> |
+  |         | anchor                                                                            | positive                                                                             |
+  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                               |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 12.45 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 126.17 tokens</li><li>max: 378 tokens</li></ul> |
 * Samples:
-  | anchor                                                                          | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
-  |:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-  | <code>Is it hard to move old systems to the cloud?</code>                       | <code>We offer custom software development, digital marketing strategies, and tailored solutions to drive tangible results for your business. Our expert team combines technical prowess with industry insights to propel your business forward in the digital landscape.<br><br>"Engage, analyze & target your customers<br>Digital transformation enables you to interact with customers across multiple channels, providing personalized experiences. This could include social media engagement, interactive websites, and mobile apps."  "Empower your employees & partners<br>The push for digital transformation has led many companies to embrace cloud solutions. However, the migration and integration of legacy systems into the cloud often present challenges."   "Optimize & automate your operations<br>The push for digital transformation has led many companies to embrace cloud solutions. However, the migration and integration of legacy systems into the cloud often present challenges."  "Transform your products<br>The push for digi...</code> |
-  | <code>What benefits does marketing automation offer for time management?</code> | <code>Our MarTech capabilities<br><br>Personalization<br>Involves tailoring marketing messages and experiences to individual customers. It enhances customer engagement, loyalty, and ultimately, conversion rates.<br><br>Marketing Automation<br>Marketing automation streamlines repetitive tasks such as email marketing, lead nurturing, and social media posting. It improves efficiency, saves time, and ensures timely communication with customers.<br><br>Customer Relationship Management<br>CRM systems help manage interactions with current and potential customers. They store customer data, track interactions, and facilitate communication, improving customer retention.</code>                                                                                                                                                                                                                                                                                                                                                                        |
-  | <code>How can your recommendation engines improve our business?</code>          | <code>How can your recommendation engines improve our business?<br>Our recommendation engines are designed to analyze customer behavior and preferences to deliver personalized suggestions, enhancing user experience, increasing sales, and boosting customer retention.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+  | anchor                                                                                              | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+  |:----------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>How can digital transformation enhance customer interactions across multiple channels?</code> | <code>We offer custom software development, digital marketing strategies, and tailored solutions to drive tangible results for your business. Our expert team combines technical prowess with industry insights to propel your business forward in the digital landscape.<br><br>"Engage, analyze & target your customers<br>Digital transformation enables you to interact with customers across multiple channels, providing personalized experiences. This could include social media engagement, interactive websites, and mobile apps."  "Empower your employees & partners<br>The push for digital transformation has led many companies to embrace cloud solutions. However, the migration and integration of legacy systems into the cloud often present challenges."   "Optimize & automate your operations<br>The push for digital transformation has led many companies to embrace cloud solutions. However, the migration and integration of legacy systems into the cloud often present challenges."  "Transform your products<br>The push for digi...</code> |
+  | <code>How does a CRM system improve customer retention?</code>                                      | <code>Our MarTech capabilities<br><br>Personalization<br>Involves tailoring marketing messages and experiences to individual customers. It enhances customer engagement, loyalty, and ultimately, conversion rates.<br><br>Marketing Automation<br>Marketing automation streamlines repetitive tasks such as email marketing, lead nurturing, and social media posting. It improves efficiency, saves time, and ensures timely communication with customers.<br><br>Customer Relationship Management<br>CRM systems help manage interactions with current and potential customers. They store customer data, track interactions, and facilitate communication, improving customer retention.</code>                                                                                                                                                                                                                                                                                                                                                                        |
+  | <code>How can your recommendation engines improve our business?</code>                              | <code>How can your recommendation engines improve our business?<br>Our recommendation engines are designed to analyze customer behavior and preferences to deliver personalized suggestions, enhancing user experience, increasing sales, and boosting customer retention.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
 * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
   ```json
   {
@@ -688,8 +687,8 @@ You can finetune this model on your own dataset.
 - `load_best_model_at_end`: True
 - `optim`: adamw_torch_fused
 - `push_to_hub`: True
-- `hub_model_id`: Shashwat13333/bge-base-en-v1.5
-- `push_to_hub_model_id`: bge-base-en-v1.5
+- `hub_model_id`: Shashwat13333/msmarco-distilbert-base-v4
+- `push_to_hub_model_id`: msmarco-distilbert-base-v4
 - `batch_sampler`: no_duplicates
 
 #### All Hyperparameters
@@ -775,7 +774,7 @@ You can finetune this model on your own dataset.
 - `use_legacy_prediction_loop`: False
 - `push_to_hub`: True
 - `resume_from_checkpoint`: None
-- `hub_model_id`: Shashwat13333/bge-base-en-v1.5
+- `hub_model_id`: Shashwat13333/msmarco-distilbert-base-v4
 - `hub_strategy`: every_save
 - `hub_private_repo`: None
 - `hub_always_push`: False
@@ -785,7 +784,7 @@ You can finetune this model on your own dataset.
 - `include_for_metrics`: []
 - `eval_do_concat_batches`: True
 - `fp16_backend`: auto
-- `push_to_hub_model_id`: bge-base-en-v1.5
+- `push_to_hub_model_id`: msmarco-distilbert-base-v4
 - `push_to_hub_organization`: None
 - `mp_parameters`: 
 - `auto_find_batch_size`: False
@@ -814,16 +813,16 @@ You can finetune this model on your own dataset.
 </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 |
-|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
-| 0.2105     | 1      | 4.4608        | -                      | -                      | -                      | -                      | -                     |
-| 0.8421     | 4      | -             | 0.3891                 | 0.3727                 | 0.4175                 | 0.3876                 | 0.2956                |
-| 1.2105     | 5      | 4.2215        | -                      | -                      | -                      | -                      | -                     |
-| 1.8421     | 8      | -             | 0.4088                 | 0.4351                 | 0.4034                 | 0.4052                 | 0.3167                |
-| 2.4211     | 10     | 3.397         | -                      | -                      | -                      | -                      | -                     |
-| 2.8421     | 12     | -             | 0.4440                 | 0.4252                 | 0.4133                 | 0.4284                 | 0.3024                |
-| 3.6316     | 15     | 2.87          | -                      | -                      | -                      | -                      | -                     |
-| **3.8421** | **16** | **-**         | **0.4371**             | **0.4332**             | **0.4384**             | **0.4444**             | **0.312**             |
+| 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 |
+|:----------:|:-----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
+| 0.2105     | 1     | 3.5757        | -                      | -                      | -                      | -                      | -                     |
+| **0.8421** | **4** | **-**         | **0.3563**             | **0.3543**             | **0.3378**             | **0.3681**             | **0.3077**            |
+| 1.2105     | 5     | 4.4031        | -                      | -                      | -                      | -                      | -                     |
+| 1.8421     | 8     | -             | 0.3652                 | 0.3547                 | 0.3574                 | 0.3542                 | 0.3579                |
+| 2.4211     | 10    | 3.3423        | -                      | -                      | -                      | -                      | -                     |
+| 2.8421     | 12    | -             | 0.3783                 | 0.3680                 | 0.3558                 | 0.3807                 | 0.3408                |
+| 3.6316     | 15    | 2.3695        | -                      | -                      | -                      | -                      | -                     |
+| 3.8421     | 16    | -             | 0.3697                 | 0.3703                 | 0.3732                 | 0.3495                 | 0.3449                |
 
 * The bold row denotes the saved checkpoint.
 

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:22f9d81dc197c7a059053b8e73dc8b5b15fb25694c53facba8b17e0c962f4a05
+oid sha256:2d91a104071bb5ba669e148ddb4754936ef42ce0974dd548e4bb32e07b965495
 size 265462608

modules.json

@@ -10,11 +10,5 @@
     "name": "1",
     "path": "1_Pooling",
     "type": "sentence_transformers.models.Pooling"
-  },
-  {
-    "idx": 2,
-    "name": "2",
-    "path": "2_Normalize",
-    "type": "sentence_transformers.models.Normalize"
   }
 ]

sentence_bert_config.json

@@ -1,4 +1,4 @@
 {
   "max_seq_length": 512,
-  "do_lower_case": true
+  "do_lower_case": false
 }