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README.md

@@ -1,7 +1,5 @@
 ---
 base_model: BAAI/bge-small-en-v1.5
-datasets: []
-language: []
 library_name: sentence-transformers
 metrics:
 - cosine_accuracy@1
@@ -19,78 +17,69 @@ metrics:
 - cosine_ndcg@10
 - cosine_mrr@10
 - cosine_map@100
-- dot_accuracy@1
-- dot_accuracy@3
-- dot_accuracy@5
-- dot_accuracy@10
-- dot_precision@1
-- dot_precision@3
-- dot_precision@5
-- dot_precision@10
-- dot_recall@1
-- dot_recall@3
-- dot_recall@5
-- dot_recall@10
-- dot_ndcg@10
-- dot_mrr@10
-- dot_map@100
 pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
 - sentence-similarity
 - feature-extraction
 - generated_from_trainer
-- dataset_size:1089
+- dataset_size:1090
 - loss:MultipleNegativesRankingLoss
 widget:
-- source_sentence: sort my holdings based on lowest fees
+- source_sentence: how do different regions contribute to my returns
   sentences:
-  - '[{"get_portfolio(None,None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''asset_class'',''us
-    equity'',''portfolio'')": "portfolio"}]'
-  - '[{"get_portfolio(None,None)": "portfolio"}, {"get_attribute(''portfolio'',[''expense
-    ratio''],''<DATES>'')": "portfolio"}, {"sort(''portfolio'',''expense ratio'',''asc'')":
-    "portfolio"}]'
-  - '[{"get_all_portfolios(''virtual'')": "virtual_portfolios"}]'
-- source_sentence: what is the volatility of each of my holdings?
-  sentences:
-  - '[{"get_portfolio(None,None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''sector'',''sector
-    industrials'',''portfolio'')": "portfolio"}]'
-  - '[{"get_portfolio([''type''],None)": "portfolio"}, {"get_attribute(''portfolio'',[''risk''],''<DATES>'')":
-    "portfolio"}, {"sort(''portfolio'',''risk'',''asc'')": "portfolio"}]'
   - '[{"get_portfolio([''type''],None)": "portfolio"}, {"filter(''portfolio'',''type'',''=='',''ETF'')":
     "portfolio"}, {"get_attribute(''portfolio'',[''losses''],''<DATES>'')": "portfolio"},
     {"filter(''portfolio'',''losses'',''<'',''0'')": "portfolio"}, {"sort(''portfolio'',''losses'',''asc'')":
     "portfolio"}]'
-- source_sentence: show region breakdown of my returns
+  - '[{"get_portfolio(None,None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''region'',None,''returns'')":
+    "portfolio"}]'
+  - '[{"get_portfolio([''marketValue''],None)": "portfolio"}, {"get_attribute(''portfolio'',[''<TICKER1>''],''<DATES>'')":
+    "portfolio"}, {"calculate(''portfolio'',[''marketValue'', ''<TICKER1>''],''multiply'',''expo_<TICKER1>'')":
+    "portfolio"}, {"sort(''portfolio'',''expo_<TICKER1>'',''desc'')": "portfolio"},
+    {"aggregate(''portfolio'',''ticker'',''expo_<TICKER1>'',''sum'',None)": "port_expo_<TICKER1>"}]'
+- source_sentence: which percent of my portfolio is in single stocks?
+  sentences:
+  - '[{"get_portfolio([''quantity'', ''averageCost'', ''marketValue''],None)": "portfolio"},
+    {"filter(''portfolio'',''ticker'',''=='',''<TICKER1>'')": "portfolio"}, {"calculate(''portfolio'',[''quantity'',
+    ''averageCost''],''multiply'',''cost_basis'')": "portfolio"}, {"calculate(''portfolio'',[''marketValue'',
+    ''cost_basis''],''difference'',''profit'')": "profit_<TICKER1>"}, {"aggregate(''portfolio'',''ticker'',''profit'',''sum'',None)":
+    "profit_<TICKER1>"}]'
+  - '[{"get_portfolio([''type''],None)": "portfolio"}, {"filter(''portfolio'',''type'',''=='',''SHARE'')":
+    "portfolio"}, {"aggregate(''portfolio'',''ticker'',''marketValue'',''sum'',None)":
+    "stocks_amount"}]'
+  - '[{"get_portfolio(None,None)": "portfolio"}, {"get_attribute(''portfolio'',[''dividend
+    yield''],''<DATES>'')": "portfolio"}, {"filter(''portfolio'',''dividend yield'',''>'',''0'')":
+    "portfolio"}, {"sort(''portfolio'',''dividend yield'',''desc'')": "portfolio"}]'
+- source_sentence: what is the volatility of each of my holdings?
   sentences:
   - '[{"get_portfolio(None,None)": "portfolio"}, {"get_attribute(''portfolio'',[''gains''],''<DATES>'')":
     "portfolio"}, {"sort(''portfolio'',''gains'',''desc'')": "portfolio"}, {"get_attribute([''<TICKER1>''],[''returns''],''<DATES>'')":
     "<TICKER1>_performance_data"}]'
   - '[{"get_portfolio(None,None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''asset_class'',''global
     bonds'',''returns'')": "portfolio"}]'
-  - '[{"get_portfolio(None,None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''region'',None,''returns'')":
-    "portfolio"}]'
-- source_sentence: show me my single stocks
+  - '[{"get_portfolio([''type''],None)": "portfolio"}, {"get_attribute(''portfolio'',[''risk''],''<DATES>'')":
+    "portfolio"}, {"sort(''portfolio'',''risk'',''asc'')": "portfolio"}]'
+- source_sentence: list all paper trading portfolios
   sentences:
-  - '[{"get_portfolio([''type''],None)": "portfolio"}, {"filter(''portfolio'',''type'',''=='',''SHARE'')":
-    "portfolio"}, {"aggregate(''portfolio'',''ticker'',''marketValue'',''sum'',None)":
-    "stocks_amount"}]'
+  - '[{"get_all_portfolios(''virtual'')": "virtual_portfolios"}]'
   - '[{"get_portfolio([''averageCost''],None)": "portfolio"}, {"get_attribute(''portfolio'',[''price''],''<DATES>'')":
     "portfolio"}, {"calculate(''portfolio'',[''price'', ''averageCost''],''difference'',''price_delta'')":
     "portfolio"}, {"filter(''portfolio'',''price_delta'',''>'',''0'')": "portfolio"},
     {"sort(''portfolio'',''price_delta'',''desc'')": "portfolio"}]'
-  - '[{"get_portfolio(None,None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''sector'',''sector
-    information technology'',''portfolio'')": "portfolio"}]'
-- source_sentence: how much do I have [TICKER] all accounts
+  - '[{"get_portfolio(None,<PORTFOLIO_NAME_1>)": "portfolio"}, {"get_attribute(''portfolio'',[''gains''],''<DATES>'')":
+    "portfolio"}, {"filter(''portfolio'',''gains'',''>'',''0'')": "portfolio"}, {"sort(''portfolio'',''gains'',''desc'')":
+    "portfolio"}]'
+- source_sentence: what is my exposure to US Equities?
   sentences:
-  - '[{"get_portfolio([''marketValue''],None)": "portfolio"}, {"get_attribute(''portfolio'',[''<TICKER1>''],''<DATES>'')":
-    "portfolio"}, {"calculate(''portfolio'',[''marketValue'', ''<TICKER1>''],''multiply'',''expo_<TICKER1>'')":
-    "portfolio"}, {"sort(''portfolio'',''expo_<TICKER1>'',''desc'')": "portfolio"},
-    {"aggregate(''portfolio'',''ticker'',''expo_<TICKER1>'',''sum'',None)": "port_expo_<TICKER1>"}]'
-  - '[{"get_portfolio(None,None)": "portfolio"}, {"analyze_impact(''portfolio'',''<TICKER1>'',''sell'')":
-    "impact_of_selling_<TICKER1>"}]'
+  - '[{"get_portfolio(None,None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''asset_class'',''us
+    equity'',''portfolio'')": "portfolio"}]'
   - '[{"get_portfolio(None,None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''sector'',''sector
-    automobiles'',''portfolio'')": "portfolio"}]'
+    industrials'',''portfolio'')": "portfolio"}]'
+  - '[{"get_portfolio([''type''],None)": "portfolio"}, {"filter(''portfolio'',''type'',''=='',''ETF'')":
+    "portfolio"}, {"get_attribute(''portfolio'',[''gains''],''<DATES>'')": "portfolio"},
+    {"filter(''portfolio'',''gains'',''>'',''0'')": "portfolio"}, {"sort(''portfolio'',''gains'',''desc'')":
+    "portfolio"}]'
 model-index:
 - name: SentenceTransformer based on BAAI/bge-small-en-v1.5
   results:
@@ -102,95 +91,50 @@ model-index:
       type: unknown
     metrics:
     - type: cosine_accuracy@1
-      value: 0.6712328767123288
+      value: 0.678082191780822
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.815068493150685
+      value: 0.8082191780821918
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.8561643835616438
+      value: 0.863013698630137
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.9178082191780822
+      value: 0.9315068493150684
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.6712328767123288
+      value: 0.678082191780822
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.27168949771689493
+      value: 0.2694063926940639
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.17123287671232873
+      value: 0.17260273972602735
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.0917808219178082
+      value: 0.09315068493150684
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.018645357686453576
+      value: 0.018835616438356163
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.02264079147640792
+      value: 0.02245053272450533
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.023782343987823442
+      value: 0.02397260273972603
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.02549467275494673
+      value: 0.025875190258751908
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.1737871975139111
+      value: 0.17595381476268288
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.7488530115242443
+      value: 0.7579120460969775
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.020899452334742528
+      value: 0.02111814463536371
       name: Cosine Map@100
-    - type: dot_accuracy@1
-      value: 0.6712328767123288
-      name: Dot Accuracy@1
-    - type: dot_accuracy@3
-      value: 0.815068493150685
-      name: Dot Accuracy@3
-    - type: dot_accuracy@5
-      value: 0.8561643835616438
-      name: Dot Accuracy@5
-    - type: dot_accuracy@10
-      value: 0.9178082191780822
-      name: Dot Accuracy@10
-    - type: dot_precision@1
-      value: 0.6712328767123288
-      name: Dot Precision@1
-    - type: dot_precision@3
-      value: 0.27168949771689493
-      name: Dot Precision@3
-    - type: dot_precision@5
-      value: 0.17123287671232873
-      name: Dot Precision@5
-    - type: dot_precision@10
-      value: 0.0917808219178082
-      name: Dot Precision@10
-    - type: dot_recall@1
-      value: 0.018645357686453576
-      name: Dot Recall@1
-    - type: dot_recall@3
-      value: 0.02264079147640792
-      name: Dot Recall@3
-    - type: dot_recall@5
-      value: 0.023782343987823442
-      name: Dot Recall@5
-    - type: dot_recall@10
-      value: 0.02549467275494673
-      name: Dot Recall@10
-    - type: dot_ndcg@10
-      value: 0.1737871975139111
-      name: Dot Ndcg@10
-    - type: dot_mrr@10
-      value: 0.7488530115242443
-      name: Dot Mrr@10
-    - type: dot_map@100
-      value: 0.020899452334742528
-      name: Dot Map@100
 ---
 
 # SentenceTransformer based on BAAI/bge-small-en-v1.5
@@ -203,7 +147,7 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [B
 - **Model Type:** Sentence Transformer
 - **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
 - **Maximum Sequence Length:** 512 tokens
-- **Output Dimensionality:** 384 tokens
+- **Output Dimensionality:** 384 dimensions
 - **Similarity Function:** Cosine Similarity
 <!-- - **Training Dataset:** Unknown -->
 <!-- - **Language:** Unknown -->
@@ -243,9 +187,9 @@ from sentence_transformers import SentenceTransformer
 model = SentenceTransformer("sentence_transformers_model_id")
 # Run inference
 sentences = [
-    'how much do I have [TICKER] all accounts',
-    '[{"get_portfolio([\'marketValue\'],None)": "portfolio"}, {"get_attribute(\'portfolio\',[\'<TICKER1>\'],\'<DATES>\')": "portfolio"}, {"calculate(\'portfolio\',[\'marketValue\', \'<TICKER1>\'],\'multiply\',\'expo_<TICKER1>\')": "portfolio"}, {"sort(\'portfolio\',\'expo_<TICKER1>\',\'desc\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'expo_<TICKER1>\',\'sum\',None)": "port_expo_<TICKER1>"}]',
-    '[{"get_portfolio(None,None)": "portfolio"}, {"analyze_impact(\'portfolio\',\'<TICKER1>\',\'sell\')": "impact_of_selling_<TICKER1>"}]',
+    'what is my exposure to US Equities?',
+    '[{"get_portfolio(None,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'asset_class\',\'us equity\',\'portfolio\')": "portfolio"}]',
+    '[{"get_portfolio(None,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',\'sector industrials\',\'portfolio\')": "portfolio"}]',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -289,38 +233,23 @@ You can finetune this model on your own dataset.
 
 * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
 
-| Metric              | Value      |
-|:--------------------|:-----------|
-| cosine_accuracy@1   | 0.6712     |
-| cosine_accuracy@3   | 0.8151     |
-| cosine_accuracy@5   | 0.8562     |
-| cosine_accuracy@10  | 0.9178     |
-| cosine_precision@1  | 0.6712     |
-| cosine_precision@3  | 0.2717     |
-| cosine_precision@5  | 0.1712     |
-| cosine_precision@10 | 0.0918     |
-| cosine_recall@1     | 0.0186     |
-| cosine_recall@3     | 0.0226     |
-| cosine_recall@5     | 0.0238     |
-| cosine_recall@10    | 0.0255     |
-| cosine_ndcg@10      | 0.1738     |
-| cosine_mrr@10       | 0.7489     |
-| **cosine_map@100**  | **0.0209** |
-| dot_accuracy@1      | 0.6712     |
-| dot_accuracy@3      | 0.8151     |
-| dot_accuracy@5      | 0.8562     |
-| dot_accuracy@10     | 0.9178     |
-| dot_precision@1     | 0.6712     |
-| dot_precision@3     | 0.2717     |
-| dot_precision@5     | 0.1712     |
-| dot_precision@10    | 0.0918     |
-| dot_recall@1        | 0.0186     |
-| dot_recall@3        | 0.0226     |
-| dot_recall@5        | 0.0238     |
-| dot_recall@10       | 0.0255     |
-| dot_ndcg@10         | 0.1738     |
-| dot_mrr@10          | 0.7489     |
-| dot_map@100         | 0.0209     |
+| Metric              | Value     |
+|:--------------------|:----------|
+| cosine_accuracy@1   | 0.6781    |
+| cosine_accuracy@3   | 0.8082    |
+| cosine_accuracy@5   | 0.863     |
+| cosine_accuracy@10  | 0.9315    |
+| cosine_precision@1  | 0.6781    |
+| cosine_precision@3  | 0.2694    |
+| cosine_precision@5  | 0.1726    |
+| cosine_precision@10 | 0.0932    |
+| cosine_recall@1     | 0.0188    |
+| cosine_recall@3     | 0.0225    |
+| cosine_recall@5     | 0.024     |
+| cosine_recall@10    | 0.0259    |
+| **cosine_ndcg@10**  | **0.176** |
+| cosine_mrr@10       | 0.7579    |
+| cosine_map@100      | 0.0211    |
 
 <!--
 ## Bias, Risks and Limitations
@@ -341,13 +270,13 @@ You can finetune this model on your own dataset.
 #### Unnamed Dataset
 
 
-* Size: 1,089 training samples
+* Size: 1,090 training samples
 * Columns: <code>sentence_0</code> and <code>sentence_1</code>
 * Approximate statistics based on the first 1000 samples:
-  |         | sentence_0                                                                       | sentence_1                                                                          |
-  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
-  | type    | string                                                                           | string                                                                              |
-  | details | <ul><li>min: 5 tokens</li><li>mean: 13.3 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 87.73 tokens</li><li>max: 196 tokens</li></ul> |
+  |         | sentence_0                                                                        | sentence_1                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                              |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 13.28 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 87.73 tokens</li><li>max: 196 tokens</li></ul> |
 * Samples:
   | sentence_0                                            | sentence_1                                                                                                                                                                   |
   |:------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
@@ -483,6 +412,7 @@ You can finetune this model on your own dataset.
 - `batch_eval_metrics`: False
 - `eval_on_start`: False
 - `eval_use_gather_object`: False
+- `prompts`: None
 - `batch_sampler`: batch_sampler
 - `multi_dataset_batch_sampler`: round_robin
 
@@ -491,155 +421,125 @@ You can finetune this model on your own dataset.
 ### Training Logs
 <details><summary>Click to expand</summary>
 
-| Epoch  | Step | cosine_map@100 |
+| Epoch  | Step | cosine_ndcg@10 |
 |:------:|:----:|:--------------:|
-| 0.0183 | 2    | 0.0136         |
-| 0.0367 | 4    | 0.0136         |
-| 0.0550 | 6    | 0.0137         |
-| 0.0734 | 8    | 0.0140         |
-| 0.0917 | 10   | 0.0141         |
-| 0.1101 | 12   | 0.0142         |
-| 0.1284 | 14   | 0.0144         |
-| 0.1468 | 16   | 0.0146         |
-| 0.1651 | 18   | 0.0147         |
-| 0.1835 | 20   | 0.0151         |
-| 0.2018 | 22   | 0.0152         |
-| 0.2202 | 24   | 0.0157         |
-| 0.2385 | 26   | 0.0159         |
-| 0.2569 | 28   | 0.0163         |
-| 0.2752 | 30   | 0.0164         |
-| 0.2936 | 32   | 0.0165         |
-| 0.3119 | 34   | 0.0165         |
-| 0.3303 | 36   | 0.0168         |
-| 0.3486 | 38   | 0.0174         |
-| 0.3670 | 40   | 0.0176         |
-| 0.3853 | 42   | 0.0180         |
-| 0.4037 | 44   | 0.0183         |
-| 0.4220 | 46   | 0.0182         |
-| 0.4404 | 48   | 0.0184         |
-| 0.4587 | 50   | 0.0184         |
-| 0.4771 | 52   | 0.0185         |
-| 0.4954 | 54   | 0.0187         |
-| 0.5138 | 56   | 0.0188         |
-| 0.5321 | 58   | 0.0190         |
-| 0.5505 | 60   | 0.0191         |
-| 0.5688 | 62   | 0.0191         |
-| 0.5872 | 64   | 0.0193         |
-| 0.6055 | 66   | 0.0192         |
-| 0.6239 | 68   | 0.0190         |
-| 0.6422 | 70   | 0.0190         |
-| 0.6606 | 72   | 0.0186         |
-| 0.6789 | 74   | 0.0185         |
-| 0.6972 | 76   | 0.0185         |
-| 0.7156 | 78   | 0.0185         |
-| 0.7339 | 80   | 0.0184         |
-| 0.7523 | 82   | 0.0185         |
-| 0.7706 | 84   | 0.0189         |
-| 0.7890 | 86   | 0.0191         |
-| 0.8073 | 88   | 0.0192         |
-| 0.8257 | 90   | 0.0195         |
-| 0.8440 | 92   | 0.0196         |
-| 0.8624 | 94   | 0.0198         |
-| 0.8807 | 96   | 0.0199         |
-| 0.8991 | 98   | 0.0200         |
-| 0.9174 | 100  | 0.0202         |
-| 0.9358 | 102  | 0.0202         |
-| 0.9541 | 104  | 0.0202         |
-| 0.9725 | 106  | 0.0203         |
-| 0.9908 | 108  | 0.0202         |
-| 1.0    | 109  | 0.0201         |
-| 1.0092 | 110  | 0.0202         |
-| 1.0275 | 112  | 0.0202         |
-| 1.0459 | 114  | 0.0202         |
-| 1.0642 | 116  | 0.0202         |
-| 1.0826 | 118  | 0.0205         |
-| 1.1009 | 120  | 0.0205         |
-| 1.1193 | 122  | 0.0205         |
-| 1.1376 | 124  | 0.0207         |
-| 1.1560 | 126  | 0.0206         |
-| 1.1743 | 128  | 0.0205         |
-| 1.1927 | 130  | 0.0204         |
-| 1.2110 | 132  | 0.0205         |
-| 1.2294 | 134  | 0.0205         |
-| 1.2477 | 136  | 0.0205         |
-| 1.2661 | 138  | 0.0203         |
-| 1.2844 | 140  | 0.0204         |
-| 1.3028 | 142  | 0.0205         |
-| 1.3211 | 144  | 0.0205         |
-| 1.3394 | 146  | 0.0206         |
-| 1.3578 | 148  | 0.0205         |
-| 1.3761 | 150  | 0.0205         |
-| 1.3945 | 152  | 0.0205         |
-| 1.4128 | 154  | 0.0205         |
-| 1.4312 | 156  | 0.0204         |
-| 1.4495 | 158  | 0.0204         |
-| 1.4679 | 160  | 0.0205         |
-| 1.4862 | 162  | 0.0205         |
-| 1.5046 | 164  | 0.0205         |
-| 1.5229 | 166  | 0.0205         |
-| 1.5413 | 168  | 0.0206         |
-| 1.5596 | 170  | 0.0206         |
-| 1.5780 | 172  | 0.0206         |
-| 1.5963 | 174  | 0.0206         |
-| 1.6147 | 176  | 0.0206         |
-| 1.6330 | 178  | 0.0205         |
-| 1.6514 | 180  | 0.0205         |
-| 1.6697 | 182  | 0.0205         |
-| 1.6881 | 184  | 0.0205         |
-| 1.7064 | 186  | 0.0205         |
-| 1.7248 | 188  | 0.0205         |
-| 1.7431 | 190  | 0.0206         |
-| 1.7615 | 192  | 0.0207         |
-| 1.7798 | 194  | 0.0207         |
-| 1.7982 | 196  | 0.0205         |
-| 1.8165 | 198  | 0.0203         |
-| 1.8349 | 200  | 0.0202         |
-| 1.8532 | 202  | 0.0201         |
-| 1.8716 | 204  | 0.0203         |
-| 1.8899 | 206  | 0.0204         |
-| 1.9083 | 208  | 0.0204         |
-| 1.9266 | 210  | 0.0204         |
-| 1.9450 | 212  | 0.0203         |
-| 1.9633 | 214  | 0.0203         |
-| 1.9817 | 216  | 0.0206         |
-| 2.0    | 218  | 0.0205         |
-| 2.0183 | 220  | 0.0206         |
-| 2.0367 | 222  | 0.0207         |
-| 2.0550 | 224  | 0.0207         |
-| 2.0734 | 226  | 0.0207         |
-| 2.0917 | 228  | 0.0206         |
-| 2.1101 | 230  | 0.0206         |
-| 2.1284 | 232  | 0.0206         |
-| 2.1468 | 234  | 0.0205         |
-| 2.1651 | 236  | 0.0205         |
-| 2.1835 | 238  | 0.0205         |
-| 2.2018 | 240  | 0.0204         |
-| 2.2202 | 242  | 0.0203         |
-| 2.2385 | 244  | 0.0203         |
-| 2.2569 | 246  | 0.0203         |
-| 2.2752 | 248  | 0.0206         |
-| 2.2936 | 250  | 0.0206         |
-| 2.3119 | 252  | 0.0206         |
-| 2.3303 | 254  | 0.0205         |
-| 2.3486 | 256  | 0.0205         |
-| 2.3670 | 258  | 0.0205         |
-| 2.3853 | 260  | 0.0204         |
-| 2.4037 | 262  | 0.0204         |
-| 2.4220 | 264  | 0.0205         |
-| 2.4404 | 266  | 0.0207         |
-| 2.4587 | 268  | 0.0207         |
-| 2.4771 | 270  | 0.0208         |
-| 2.4954 | 272  | 0.0206         |
-| 2.5138 | 274  | 0.0207         |
-| 2.5321 | 276  | 0.0208         |
-| 2.5505 | 278  | 0.0208         |
-| 2.5688 | 280  | 0.0209         |
+| 0.0183 | 2    | 0.1179         |
+| 0.0367 | 4    | 0.1184         |
+| 0.0550 | 6    | 0.1193         |
+| 0.0734 | 8    | 0.1201         |
+| 0.0917 | 10   | 0.1227         |
+| 0.1101 | 12   | 0.1235         |
+| 0.1284 | 14   | 0.1255         |
+| 0.1468 | 16   | 0.1267         |
+| 0.1651 | 18   | 0.1299         |
+| 0.1835 | 20   | 0.1320         |
+| 0.2018 | 22   | 0.1348         |
+| 0.2202 | 24   | 0.1367         |
+| 0.2385 | 26   | 0.1383         |
+| 0.2569 | 28   | 0.1413         |
+| 0.2752 | 30   | 0.1420         |
+| 0.2936 | 32   | 0.1432         |
+| 0.3119 | 34   | 0.1435         |
+| 0.3303 | 36   | 0.1451         |
+| 0.3486 | 38   | 0.1471         |
+| 0.3670 | 40   | 0.1491         |
+| 0.3853 | 42   | 0.1503         |
+| 0.4037 | 44   | 0.1523         |
+| 0.4220 | 46   | 0.1525         |
+| 0.4404 | 48   | 0.1531         |
+| 0.4587 | 50   | 0.1535         |
+| 0.4771 | 52   | 0.1534         |
+| 0.4954 | 54   | 0.1529         |
+| 0.5138 | 56   | 0.1528         |
+| 0.5321 | 58   | 0.1556         |
+| 0.5505 | 60   | 0.1568         |
+| 0.5688 | 62   | 0.1576         |
+| 0.5872 | 64   | 0.1577         |
+| 0.6055 | 66   | 0.1577         |
+| 0.6239 | 68   | 0.1575         |
+| 0.6422 | 70   | 0.1586         |
+| 0.6606 | 72   | 0.1596         |
+| 0.6789 | 74   | 0.1612         |
+| 0.6972 | 76   | 0.1617         |
+| 0.7156 | 78   | 0.1637         |
+| 0.7339 | 80   | 0.1638         |
+| 0.7523 | 82   | 0.1637         |
+| 0.7706 | 84   | 0.1635         |
+| 0.7890 | 86   | 0.1634         |
+| 0.8073 | 88   | 0.1640         |
+| 0.8257 | 90   | 0.1641         |
+| 0.8440 | 92   | 0.1652         |
+| 0.8624 | 94   | 0.1652         |
+| 0.8807 | 96   | 0.1657         |
+| 0.8991 | 98   | 0.1650         |
+| 0.9174 | 100  | 0.1664         |
+| 0.9358 | 102  | 0.1668         |
+| 0.9541 | 104  | 0.1671         |
+| 0.9725 | 106  | 0.1683         |
+| 0.9908 | 108  | 0.1689         |
+| 1.0    | 109  | 0.1684         |
+| 1.0092 | 110  | 0.1673         |
+| 1.0275 | 112  | 0.1686         |
+| 1.0459 | 114  | 0.1680         |
+| 1.0642 | 116  | 0.1676         |
+| 1.0826 | 118  | 0.1668         |
+| 1.1009 | 120  | 0.1668         |
+| 1.1193 | 122  | 0.1671         |
+| 1.1376 | 124  | 0.1673         |
+| 1.1560 | 126  | 0.1666         |
+| 1.1743 | 128  | 0.1669         |
+| 1.1927 | 130  | 0.1668         |
+| 1.2110 | 132  | 0.1669         |
+| 1.2294 | 134  | 0.1673         |
+| 1.2477 | 136  | 0.1681         |
+| 1.2661 | 138  | 0.1683         |
+| 1.2844 | 140  | 0.1681         |
+| 1.3028 | 142  | 0.1674         |
+| 1.3211 | 144  | 0.1672         |
+| 1.3394 | 146  | 0.1668         |
+| 1.3578 | 148  | 0.1682         |
+| 1.3761 | 150  | 0.1689         |
+| 1.3945 | 152  | 0.1690         |
+| 1.4128 | 154  | 0.1693         |
+| 1.4312 | 156  | 0.1683         |
+| 1.4495 | 158  | 0.1683         |
+| 1.4679 | 160  | 0.1678         |
+| 1.4862 | 162  | 0.1695         |
+| 1.5046 | 164  | 0.1710         |
+| 1.5229 | 166  | 0.1717         |
+| 1.5413 | 168  | 0.1715         |
+| 1.5596 | 170  | 0.1698         |
+| 1.5780 | 172  | 0.1699         |
+| 1.5963 | 174  | 0.1694         |
+| 1.6147 | 176  | 0.1701         |
+| 1.6330 | 178  | 0.1693         |
+| 1.6514 | 180  | 0.1683         |
+| 1.6697 | 182  | 0.1692         |
+| 1.6881 | 184  | 0.1689         |
+| 1.7064 | 186  | 0.1696         |
+| 1.7248 | 188  | 0.1696         |
+| 1.7431 | 190  | 0.1700         |
+| 1.7615 | 192  | 0.1705         |
+| 1.7798 | 194  | 0.1718         |
+| 1.7982 | 196  | 0.1719         |
+| 1.8165 | 198  | 0.1723         |
+| 1.8349 | 200  | 0.1721         |
+| 1.8532 | 202  | 0.1717         |
+| 1.8716 | 204  | 0.1722         |
+| 1.8899 | 206  | 0.1722         |
+| 1.9083 | 208  | 0.1728         |
+| 1.9266 | 210  | 0.1734         |
+| 1.9450 | 212  | 0.1733         |
+| 1.9633 | 214  | 0.1742         |
+| 1.9817 | 216  | 0.1749         |
+| 2.0    | 218  | 0.1750         |
+| 2.0183 | 220  | 0.1760         |
 
 </details>
 
 ### Framework Versions
 - Python: 3.10.9
-- Sentence Transformers: 3.0.1
+- Sentence Transformers: 3.3.1
 - Transformers: 4.44.0
 - PyTorch: 2.4.0+cu121
 - Accelerate: 0.33.0
@@ -666,7 +566,7 @@ You can finetune this model on your own dataset.
 #### MultipleNegativesRankingLoss
 ```bibtex
 @misc{henderson2017efficient,
-    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
     author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
     year={2017},
     eprint={1705.00652},

config_sentence_transformers.json

@@ -1,10 +1,10 @@
 {
   "__version__": {
-    "sentence_transformers": "3.0.1",
+    "sentence_transformers": "3.3.1",
     "transformers": "4.44.0",
     "pytorch": "2.4.0+cu121"
   },
   "prompts": {},
   "default_prompt_name": null,
-  "similarity_fn_name": null
+  "similarity_fn_name": "cosine"
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b9a877018680fb61a472627e468678bc0b81a90b2b989d167914ab65eba0ed13
+oid sha256:1600be1ae30bbefa7dba2d3fb026b69a5459d9e461cfa33ea1d7b4f64e0f77fd
 size 133462128