Updated Weights

README.md

@@ -4,38 +4,37 @@ tags:
 - sentence-similarity
 - feature-extraction
 - generated_from_trainer
-- dataset_size:2620
+- dataset_size:2640
 - loss:MultipleNegativesRankingLoss
 - loss:CosineSimilarityLoss
 base_model: jinaai/jina-embedding-b-en-v1
 widget:
-- source_sentence: What sector am I most heavily invested in?
+- source_sentence: Can you tell me how my portfolio did last week?
   sentences:
-  - 'Show me how to switch my stock portfolio to mutual funds
-
-    '
-  - What percentage of my portfolio is in X
-  - Which sector do I invest most in?
-- source_sentence: Can you tell me how my portfolio ranks among others?
+  - Suggest recommendations for me
+  - Do you have any insights on my portfolio
+  - How did my portfolio perform last week ?
+- source_sentence: What are my most risky holdings?
   sentences:
-  - What is my AMC wise split ?
-  - In which funds am I paying highest fees
-  - Compare my portfolio with others?
-- source_sentence: Which of my funds has the highest risk level?
+  - View my holdings
+  - Show my market cap breakdown
+  - Show my riskiest holdings
+- source_sentence: What profits do I have in my portfolio?
   sentences:
-  - Give me python code to find best funds in my portfolio
-  - Show my stocks ranked by performance
-  - Show my riskiest mutual funds
-- source_sentence: What's going right with my portfolio?
+  - How can I swap my stocks for mutual funds?
+  - Show me the cash in my portfolio?
+  - What are the profits I have gained in my portfolio
+- source_sentence: I'm curious, which investments have the highest volatility in my
+    portfolio?
   sentences:
-  - Is my portfolio linked?
-  - My portfolio returns over all the years
-  - What's going well in my portfolio
-- source_sentence: I'd like to know the percentage of large cap in my investments.
+  - Which sector do I invest most in?
+  - is there anything wrong with my investments?
+  - Which of my investments have the highest volatility?
+- source_sentence: Sort my investment portfolio by ESG rating, please.
   sentences:
-  - Show my riskiest holdings
-  - Can you show what percentage of my portfolio consists of large cap
-  - What is the expected return of my portfolio?
+  - What stock makes up the largest percentage of my portfolio?
+  - Can you show my worst performing holdings
+  - Show my investments sorted by ESG rating.
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
 metrics:
@@ -65,10 +64,10 @@ model-index:
       type: test-eval
     metrics:
     - type: cosine_accuracy@1
-      value: 0.8625954198473282
+      value: 0.8636363636363636
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.9961832061068703
+      value: 0.9924242424242424
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
       value: 1.0
@@ -77,10 +76,10 @@ model-index:
       value: 1.0
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.8625954198473282
+      value: 0.8636363636363636
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.33206106870229
+      value: 0.3308080808080807
       name: Cosine Precision@3
     - type: cosine_precision@5
       value: 0.19999999999999998
@@ -89,10 +88,10 @@ model-index:
       value: 0.09999999999999999
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.8625954198473282
+      value: 0.8636363636363636
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.9961832061068703
+      value: 0.9924242424242424
       name: Cosine Recall@3
     - type: cosine_recall@5
       value: 1.0
@@ -101,13 +100,13 @@ model-index:
       value: 1.0
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.9460250731496836
+      value: 0.9436916551342168
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.9271628498727736
+      value: 0.9242424242424244
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.9271628498727736
+      value: 0.9242424242424242
       name: Cosine Map@100
 ---
 
@@ -160,9 +159,9 @@ from sentence_transformers import SentenceTransformer
 model = SentenceTransformer("sentence_transformers_model_id")
 # Run inference
 sentences = [
-    "I'd like to know the percentage of large cap in my investments.",
-    'Can you show what percentage of my portfolio consists of large cap',
-    'Show my riskiest holdings',
+    'Sort my investment portfolio by ESG rating, please.',
+    'Show my investments sorted by ESG rating.',
+    'Can you show my worst performing holdings',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -207,23 +206,23 @@ You can finetune this model on your own dataset.
 * Dataset: `test-eval`
 * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
 
-| Metric              | Value     |
-|:--------------------|:----------|
-| cosine_accuracy@1   | 0.8626    |
-| cosine_accuracy@3   | 0.9962    |
-| cosine_accuracy@5   | 1.0       |
-| cosine_accuracy@10  | 1.0       |
-| cosine_precision@1  | 0.8626    |
-| cosine_precision@3  | 0.3321    |
-| cosine_precision@5  | 0.2       |
-| cosine_precision@10 | 0.1       |
-| cosine_recall@1     | 0.8626    |
-| cosine_recall@3     | 0.9962    |
-| cosine_recall@5     | 1.0       |
-| cosine_recall@10    | 1.0       |
-| **cosine_ndcg@10**  | **0.946** |
-| cosine_mrr@10       | 0.9272    |
-| cosine_map@100      | 0.9272    |
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.8636     |
+| cosine_accuracy@3   | 0.9924     |
+| cosine_accuracy@5   | 1.0        |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.8636     |
+| cosine_precision@3  | 0.3308     |
+| cosine_precision@5  | 0.2        |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.8636     |
+| cosine_recall@3     | 0.9924     |
+| cosine_recall@5     | 1.0        |
+| cosine_recall@10    | 1.0        |
+| **cosine_ndcg@10**  | **0.9437** |
+| cosine_mrr@10       | 0.9242     |
+| cosine_map@100      | 0.9242     |
 
 <!--
 ## Bias, Risks and Limitations
@@ -243,19 +242,19 @@ You can finetune this model on your own dataset.
 
 #### Unnamed Dataset
 
-* Size: 1,310 training samples
+* Size: 1,320 training samples
 * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
 * Approximate statistics based on the first 1000 samples:
   |         | sentence_0                                                                        | sentence_1                                                                       | label                                                         |
   |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------|
   | type    | string                                                                            | string                                                                           | float                                                         |
-  | details | <ul><li>min: 4 tokens</li><li>mean: 10.62 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.06 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 10.62 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.06 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
 * Samples:
-  | sentence_0                                                          | sentence_1                                                         | label            |
-  |:--------------------------------------------------------------------|:-------------------------------------------------------------------|:-----------------|
-  | <code>are there any of my funds that are lagging behind</code>      | <code>do I hold any funds that haven't been performing well</code> | <code>1.0</code> |
-  | <code>Which sectors are performing the best in my portfolio?</code> | <code>What are my best performing sectors?</code>                  | <code>1.0</code> |
-  | <code>List some of my top holdings</code>                           | <code>Show some of my best performing holdings</code>              | <code>1.0</code> |
+  | sentence_0                                             | sentence_1                                         | label            |
+  |:-------------------------------------------------------|:---------------------------------------------------|:-----------------|
+  | <code>How does my portfolio score look?</code>         | <code>What is my portfolio score?</code>           | <code>1.0</code> |
+  | <code>Show me the risk profile of my portfolio.</code> | <code>Details on my portfolio risk</code>          | <code>1.0</code> |
+  | <code>Which of my shares are the most erratic?</code>  | <code>Which of my stocks are most volatile?</code> | <code>1.0</code> |
 * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
   ```json
   {
@@ -266,19 +265,19 @@ You can finetune this model on your own dataset.
 
 #### Unnamed Dataset
 
-* Size: 1,310 training samples
+* Size: 1,320 training samples
 * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
 * Approximate statistics based on the first 1000 samples:
   |         | sentence_0                                                                        | sentence_1                                                                       | label                                                         |
   |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------|
   | type    | string                                                                            | string                                                                           | float                                                         |
-  | details | <ul><li>min: 4 tokens</li><li>mean: 10.68 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.13 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 10.62 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.05 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
 * Samples:
-  | sentence_0                                                          | sentence_1                                                | label            |
-  |:--------------------------------------------------------------------|:----------------------------------------------------------|:-----------------|
-  | <code>I need my portfolio to hit 1000% returns by next month</code> | <code>make my portfolio return 1000% by next month</code> | <code>1.0</code> |
-  | <code>What are my stocks?</code>                                    | <code>Show my stocks</code>                               | <code>1.0</code> |
-  | <code>I'd like to know my sector distribution.</code>               | <code>What is my sector allocation?</code>                | <code>1.0</code> |
+  | sentence_0                                                         | sentence_1                                                      | label            |
+  |:-------------------------------------------------------------------|:----------------------------------------------------------------|:-----------------|
+  | <code>What holdings carry the least risk in my portfolio?</code>   | <code>What are the least risky holdings in my portfolio?</code> | <code>1.0</code> |
+  | <code>How have my investments fared over the previous year?</code> | <code>How has my portfolio performed over the last year?</code> | <code>1.0</code> |
+  | <code>How well is my portfolio performing?</code>                  | <code>How is my portfolio performing</code>                     | <code>1.0</code> |
 * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
   ```json
   {
@@ -416,22 +415,20 @@ You can finetune this model on your own dataset.
 </details>
 
 ### Training Logs
-| Epoch   | Step | Training Loss | test-eval_cosine_ndcg@10 |
-|:-------:|:----:|:-------------:|:------------------------:|
-| 1.0     | 82   | -             | 0.8929                   |
-| 2.0     | 164  | -             | 0.9007                   |
-| 3.0     | 246  | -             | 0.9112                   |
-| 4.0     | 328  | -             | 0.9188                   |
-| 5.0     | 410  | -             | 0.9285                   |
-| 6.0     | 492  | -             | 0.9286                   |
-| 6.0976  | 500  | 0.2352        | 0.9291                   |
-| 7.0     | 574  | -             | 0.9356                   |
-| 8.0     | 656  | -             | 0.9404                   |
-| 9.0     | 738  | -             | 0.9406                   |
-| 10.0    | 820  | -             | 0.9434                   |
-| 11.0    | 902  | -             | 0.9424                   |
-| 12.0    | 984  | -             | 0.9455                   |
-| 12.1951 | 1000 | 0.164         | 0.9460                   |
+| Epoch  | Step | Training Loss | test-eval_cosine_ndcg@10 |
+|:------:|:----:|:-------------:|:------------------------:|
+| 1.0    | 84   | -             | 0.8877                   |
+| 2.0    | 168  | -             | 0.8944                   |
+| 3.0    | 252  | -             | 0.9042                   |
+| 4.0    | 336  | -             | 0.9123                   |
+| 5.0    | 420  | -             | 0.9241                   |
+| 5.9524 | 500  | 0.2478        | 0.9209                   |
+| 6.0    | 504  | -             | 0.9209                   |
+| 7.0    | 588  | -             | 0.9261                   |
+| 8.0    | 672  | -             | 0.9327                   |
+| 9.0    | 756  | -             | 0.9364                   |
+| 10.0   | 840  | -             | 0.9370                   |
+| 11.0   | 924  | -             | 0.9437                   |
 
 
 ### Framework Versions

eval/Information-Retrieval_evaluation_test-eval_results.csv

@@ -282,3 +282,55 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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