Training in progress, epoch 0

1_Pooling/config.json

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

README.md

@@ -10,7 +10,7 @@ tags:
 - dataset_size:150
 - loss:MatryoshkaLoss
 - loss:MultipleNegativesRankingLoss
-base_model: sentence-transformers/msmarco-distilbert-base-v4
+base_model: BAAI/bge-base-en-v1.5
 widget:
 - source_sentence: What services does Techchefz Digital offer for AI adoption?
   sentences:
@@ -34,7 +34,7 @@ widget:
     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: Do you provide support 24/7?
+- source_sentence: Hav you made any services for schools and students?
   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: What kind of data do you leverage for AI solutions?
+- source_sentence: How did TechChefz evolve from its early days?
   sentences:
   - 'Why do we need Microservices ?
 
@@ -123,38 +123,6 @@ widget:
     Technology Diversity
 
     Continuous Delivery'
-  - 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
-    harnessing the predictive power of artificial intelligence to forge the next wave
-    of software excellence. We don't just deliver AI; we deliver the future.
-  - '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.'
-- source_sentence: How did TechChefz evolve from its early days?
-  sentences:
-  - 'Our Solutions
-
-    Strategy & Digital Transformation
-
-    Innovate via digital transformation, modernize tech, craft product strategies,
-    enhance customer experiences, optimize data analytics, transition to cloud for
-    growth and efficiency
-
-
-    Product Engineering & Custom Development
-
-    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.'
   - 'After a transformative scuba dive in the Maldives, Mayank Maggon made a pivotal
     decision to depart from the corporate ladder in December 2016. Fueled by a clear
     vision to revolutionize the digital landscape, Mayank set out to leverage the
@@ -177,7 +145,42 @@ 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.'
-- source_sentence: What do you guys do for digital strategy?
+  - '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.'
+- 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
+
+    Innovate via digital transformation, modernize tech, craft product strategies,
+    enhance customer experiences, optimize data analytics, transition to cloud for
+    growth and efficiency
+
+
+    Product Engineering & Custom Development
+
+    Providing product development, enterprise web and mobile development, microservices
+    integrations, quality engineering, and application support services to drive innovation
+    and enhance operational efficiency.'
+  - 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
+    harnessing the predictive power of artificial intelligence to forge the next wave
+    of software excellence. We don't just deliver AI; we deliver the future.
+- source_sentence: What managed services does TechChefz provide ?
   sentences:
   - "  What we do\n\nDigital Strategy\nCreating digital frameworks that transform\
     \ your digital enterprise and produce a return on investment.\n\nPlatform Selection\n\
@@ -190,6 +193,12 @@ 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
@@ -213,15 +222,6 @@ 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: 0.02666666666666667
+      value: 0.17333333333333334
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.49333333333333335
+      value: 0.5466666666666666
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.5733333333333334
+      value: 0.6
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
       value: 0.6933333333333334
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.02666666666666667
+      value: 0.17333333333333334
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.16444444444444442
+      value: 0.1822222222222222
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.11466666666666664
+      value: 0.12
       name: Cosine Precision@5
     - type: cosine_precision@10
       value: 0.06933333333333333
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.02666666666666667
+      value: 0.17333333333333334
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.49333333333333335
+      value: 0.5466666666666666
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.5733333333333334
+      value: 0.6
       name: Cosine Recall@5
     - type: cosine_recall@10
       value: 0.6933333333333334
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.35508522450527796
+      value: 0.43705488094312567
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.24601587301587297
+      value: 0.3539576719576719
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.260452237646067
+      value: 0.3663753684578632
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -303,49 +303,49 @@ model-index:
       type: dim_512
     metrics:
     - type: cosine_accuracy@1
-      value: 0.06666666666666667
+      value: 0.17333333333333334
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.52
+      value: 0.5333333333333333
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.5733333333333334
+      value: 0.6266666666666667
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.7333333333333333
+      value: 0.6933333333333334
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.06666666666666667
+      value: 0.17333333333333334
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.17333333333333334
+      value: 0.17777777777777776
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.11466666666666664
+      value: 0.12533333333333332
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.0733333333333333
+      value: 0.06933333333333333
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.06666666666666667
+      value: 0.17333333333333334
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.52
+      value: 0.5333333333333333
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.5733333333333334
+      value: 0.6266666666666667
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.7333333333333333
+      value: 0.6933333333333334
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.38641175032218505
+      value: 0.43324477959330543
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.27583068783068776
+      value: 0.3495185185185184
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.2865406223728409
+      value: 0.359896266319179
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -355,49 +355,49 @@ model-index:
       type: dim_256
     metrics:
     - type: cosine_accuracy@1
-      value: 0.02666666666666667
+      value: 0.22666666666666666
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.4533333333333333
+      value: 0.49333333333333335
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.5333333333333333
+      value: 0.56
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.7066666666666667
+      value: 0.68
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.02666666666666667
+      value: 0.22666666666666666
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.15111111111111108
+      value: 0.16444444444444445
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.10666666666666667
+      value: 0.11199999999999997
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.07066666666666666
+      value: 0.06799999999999998
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.02666666666666667
+      value: 0.22666666666666666
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.4533333333333333
+      value: 0.49333333333333335
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.5333333333333333
+      value: 0.56
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.7066666666666667
+      value: 0.68
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.3453164244857898
+      value: 0.4383628839300849
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.23110582010582006
+      value: 0.36210582010582004
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.24333641899597835
+      value: 0.3731640827722892
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -407,49 +407,49 @@ model-index:
       type: dim_128
     metrics:
     - type: cosine_accuracy@1
-      value: 0.02666666666666667
+      value: 0.24
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.4666666666666667
+      value: 0.48
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.5466666666666666
+      value: 0.56
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.68
+      value: 0.6933333333333334
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.02666666666666667
+      value: 0.24
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.15555555555555556
+      value: 0.16
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.10933333333333332
+      value: 0.11199999999999997
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.06799999999999999
+      value: 0.06933333333333332
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.02666666666666667
+      value: 0.24
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.4666666666666667
+      value: 0.48
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.5466666666666666
+      value: 0.56
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.68
+      value: 0.6933333333333334
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.34067981351267523
+      value: 0.4443870388298522
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.23201587301587295
+      value: 0.36651322751322746
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.24399797704322276
+      value: 0.37546675549059694
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -459,61 +459,61 @@ model-index:
       type: dim_64
     metrics:
     - type: cosine_accuracy@1
-      value: 0.02666666666666667
+      value: 0.08
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.38666666666666666
+      value: 0.3466666666666667
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.5066666666666667
+      value: 0.49333333333333335
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.64
+      value: 0.56
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.02666666666666667
+      value: 0.08
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.1288888888888889
+      value: 0.11555555555555555
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.10133333333333333
+      value: 0.09866666666666667
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.064
+      value: 0.05599999999999999
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.02666666666666667
+      value: 0.08
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.38666666666666666
+      value: 0.3466666666666667
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.5066666666666667
+      value: 0.49333333333333335
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.64
+      value: 0.56
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.3200422311786011
+      value: 0.3120295466486537
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.21788359788359785
+      value: 0.23260846560846554
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.22860563412008764
+      value: 0.24731947636993173
       name: Cosine Map@100
 ---
 
 # BGE base Financial Matryoshka
 
-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.
+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.
 
 ## Model Details
 
 ### Model Description
 - **Model Type:** Sentence Transformer
-- **Base model:** [sentence-transformers/msmarco-distilbert-base-v4](https://huggingface.co/sentence-transformers/msmarco-distilbert-base-v4) <!-- at revision 19f0f4c73dc418bad0e0fc600611e808b7448a28 -->
+- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
 - **Maximum Sequence Length:** 512 tokens
 - **Output Dimensionality:** 768 dimensions
 - **Similarity Function:** Cosine Similarity
@@ -531,8 +531,9 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [s
 
 ```
 SentenceTransformer(
-  (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})
+  (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()
 )
 ```
 
@@ -551,12 +552,12 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("Shashwat13333/msmarco-distilbert-base-v4")
+model = SentenceTransformer("Shashwat13333/bge-base-en-v1.5")
 # Run inference
 sentences = [
-    'What do you guys do for digital strategy?',
+    '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',
-    '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',
+    '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',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -601,23 +602,23 @@ 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   | 0.0267     | 0.0667     | 0.0267     | 0.0267     | 0.0267   |
-| cosine_accuracy@3   | 0.4933     | 0.52       | 0.4533     | 0.4667     | 0.3867   |
-| cosine_accuracy@5   | 0.5733     | 0.5733     | 0.5333     | 0.5467     | 0.5067   |
-| cosine_accuracy@10  | 0.6933     | 0.7333     | 0.7067     | 0.68       | 0.64     |
-| cosine_precision@1  | 0.0267     | 0.0667     | 0.0267     | 0.0267     | 0.0267   |
-| cosine_precision@3  | 0.1644     | 0.1733     | 0.1511     | 0.1556     | 0.1289   |
-| cosine_precision@5  | 0.1147     | 0.1147     | 0.1067     | 0.1093     | 0.1013   |
-| cosine_precision@10 | 0.0693     | 0.0733     | 0.0707     | 0.068      | 0.064    |
-| cosine_recall@1     | 0.0267     | 0.0667     | 0.0267     | 0.0267     | 0.0267   |
-| cosine_recall@3     | 0.4933     | 0.52       | 0.4533     | 0.4667     | 0.3867   |
-| cosine_recall@5     | 0.5733     | 0.5733     | 0.5333     | 0.5467     | 0.5067   |
-| cosine_recall@10    | 0.6933     | 0.7333     | 0.7067     | 0.68       | 0.64     |
-| **cosine_ndcg@10**  | **0.3551** | **0.3864** | **0.3453** | **0.3407** | **0.32** |
-| cosine_mrr@10       | 0.246      | 0.2758     | 0.2311     | 0.232      | 0.2179   |
-| cosine_map@100      | 0.2605     | 0.2865     | 0.2433     | 0.244      | 0.2286   |
+| 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    |
 
 <!--
 ## Bias, Risks and Limitations
@@ -641,10 +642,10 @@ 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: 8 tokens</li><li>mean: 12.17 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: 12.4 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> |
 * Samples:
   | anchor                                                                          | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
   |:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
@@ -687,8 +688,8 @@ 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/msmarco-distilbert-base-v4
-- `push_to_hub_model_id`: msmarco-distilbert-base-v4
+- `hub_model_id`: Shashwat13333/bge-base-en-v1.5
+- `push_to_hub_model_id`: bge-base-en-v1.5
 - `batch_sampler`: no_duplicates
 
 #### All Hyperparameters
@@ -774,7 +775,7 @@ 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/msmarco-distilbert-base-v4
+- `hub_model_id`: Shashwat13333/bge-base-en-v1.5
 - `hub_strategy`: every_save
 - `hub_private_repo`: None
 - `hub_always_push`: False
@@ -784,7 +785,7 @@ 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`: msmarco-distilbert-base-v4
+- `push_to_hub_model_id`: bge-base-en-v1.5
 - `push_to_hub_organization`: None
 - `mp_parameters`: 
 - `auto_find_batch_size`: False
@@ -813,16 +814,16 @@ 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     | 4.338         | -                      | -                      | -                      | -                      | -                     |
-| **0.8421** | **4** | **-**         | **0.3546**             | **0.3692**             | **0.3343**             | **0.332**              | **0.2959**            |
-| 1.2105     | 5     | 5.7148        | -                      | -                      | -                      | -                      | -                     |
-| 1.8421     | 8     | -             | 0.3573                 | 0.3721                 | 0.3395                 | 0.3376                 | 0.3115                |
-| 2.4211     | 10    | 4.5503        | -                      | -                      | -                      | -                      | -                     |
-| 2.8421     | 12    | -             | 0.3658                 | 0.3864                 | 0.3447                 | 0.3335                 | 0.3160                |
-| 3.6316     | 15    | 4.006         | -                      | -                      | -                      | -                      | -                     |
-| 3.8421     | 16    | -             | 0.3551                 | 0.3864                 | 0.3453                 | 0.3407                 | 0.3200                |
+| 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**             |
 
 * The bold row denotes the saved checkpoint.
 

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:2d91a104071bb5ba669e148ddb4754936ef42ce0974dd548e4bb32e07b965495
 size 265462608

modules.json

@@ -10,5 +10,11 @@
     "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": false
+  "do_lower_case": true
 }

training_args.bin

@@ -1,3 +1,3 @@
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-oid sha256:70b51c278270b5e837268f738cb2399738d891ab1fdb9174ac592e71aec3f1ae
+oid sha256:acda547ef8c30f593bf86268f13c3858cbb6cc76828a1044452f749033019e66
 size 5752