Add new SentenceTransformer model

README.md

@@ -5,41 +5,42 @@ tags:
 - feature-extraction
 - dense
 - generated_from_trainer
-- dataset_size:713743
+- dataset_size:111468
 - loss:MultipleNegativesRankingLoss
 base_model: sentence-transformers/all-MiniLM-L6-v2
 widget:
-- source_sentence: 'Abraham Lincoln: Why is the Gettysburg Address so memorable?'
+- source_sentence: What is something you do (or don’t do), even though you feel conflicted
+    about it?
   sentences:
-  - 'Abraham Lincoln: Why is the Gettysburg Address so memorable?'
-  - What does the Gettysburg Address really mean?
-  - What is eatalo.com?
-- source_sentence: Has the influence of Ancient Carthage in science, math, and society
-    been underestimated?
+  - What is something you do (or don’t do), even though you feel conflicted about
+    it?
+  - Is it worth buying the iPhone 7?
+  - 'Hypothetical scenarios: King Henry VIII loses his battle with James IV in 1513
+    & dies; Pope Julius II doesn''t die in 1513. How''s the world different?'
+- source_sentence: Exams for a mechanical engineer?
   sentences:
-  - How does one earn money online without an investment from home?
-  - Has the influence of Ancient Carthage in science, math, and society been underestimated?
-  - Has the influence of the Ancient Etruscans in science and math been underestimated?
-- source_sentence: Is there any app that shares charging to others like share it how
-    we transfer files?
+  - Exams for a mechanical engineer?
+  - Can you prefer any website or ideas by which I can understand antenna subject
+    practically in b.tech?
+  - Mackenzie is a writer-in-residence at the 2B Theatre in Halifax and teaches at
+    the National Theatre School of Canada in Montreal .
+- source_sentence: What will a Christian wife do if her husband left her for years?
   sentences:
-  - How do you think of Chinese claims that the present Private Arbitration is illegal,
-    its verdict violates the UNCLOS and is illegal?
-  - Is there any app that shares charging to others like share it how we transfer
-    files?
-  - Are there any platforms that provides end-to-end encryption for file transfer/
-    sharing?
-- source_sentence: Why AAP’s MLA Dinesh Mohaniya has been arrested?
+  - How many United States Presidents have there been?
+  - What is planning without words?
+  - What will a Christian wife do if her husband left her for years?
+- source_sentence: How do I research for MUN?
   sentences:
-  - What are your views on the latest sex scandal by AAP MLA Sandeep Kumar?
-  - What is a dc current? What are some examples?
-  - Why AAP’s MLA Dinesh Mohaniya has been arrested?
-- source_sentence: What is the difference between economic growth and economic development?
+  - How do I research for MUN?
+  - What is the best way to be an investment banker?
+  - What is the best way to do an MUN research?
+- source_sentence: I am poor, ugly, untalented, 20 years old, and have big dreams.
+    How can I succeed in life?
   sentences:
-  - How cold can the Gobi Desert get, and how do its average temperatures compare
-    to the ones in the Simpson Desert?
-  - the difference between economic growth and economic development is What?
-  - What is the difference between economic growth and economic development?
+  - What app can I use taking notes?
+  - Am I too old to succeed in my life at age 32?
+  - I am poor, ugly, untalented, 20 years old, and have big dreams. How can I succeed
+    in life?
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
 metrics:
@@ -69,49 +70,49 @@ model-index:
       type: NanoMSMARCO
     metrics:
     - type: cosine_accuracy@1
-      value: 0.26
+      value: 0.28
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.52
+      value: 0.38
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.6
+      value: 0.42
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.62
+      value: 0.56
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.26
+      value: 0.28
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.1733333333333333
+      value: 0.12666666666666665
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.12
+      value: 0.084
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.062
+      value: 0.05600000000000001
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.26
+      value: 0.28
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.52
+      value: 0.38
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.6
+      value: 0.42
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.62
+      value: 0.56
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.45904886208148177
+      value: 0.4001173610020243
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.40519047619047627
+      value: 0.3516904761904761
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.4260102142025637
+      value: 0.37336992686291426
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -121,49 +122,49 @@ model-index:
       type: NanoNQ
     metrics:
     - type: cosine_accuracy@1
-      value: 0.32
+      value: 0.24
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.5
+      value: 0.32
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.6
+      value: 0.38
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.62
+      value: 0.44
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.32
+      value: 0.24
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.1733333333333333
+      value: 0.10666666666666665
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.128
+      value: 0.07600000000000001
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.066
+      value: 0.046
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.3
+      value: 0.23
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.47
+      value: 0.3
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.58
+      value: 0.35
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.6
+      value: 0.42
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.4619884812398348
+      value: 0.32272214750507383
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.4272222222222222
+      value: 0.30133333333333334
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.42411471333193373
+      value: 0.30267489572313894
       name: Cosine Map@100
   - task:
       type: nano-beir
@@ -173,49 +174,49 @@ model-index:
       type: NanoBEIR_mean
     metrics:
     - type: cosine_accuracy@1
-      value: 0.29000000000000004
+      value: 0.26
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.51
+      value: 0.35
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.6
+      value: 0.4
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.62
+      value: 0.5
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.29000000000000004
+      value: 0.26
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.1733333333333333
+      value: 0.11666666666666664
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.124
+      value: 0.08000000000000002
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.064
+      value: 0.051000000000000004
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.28
+      value: 0.255
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.495
+      value: 0.33999999999999997
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.59
+      value: 0.385
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.61
+      value: 0.49
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.4605186716606583
+      value: 0.36141975425354905
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.41620634920634925
+      value: 0.3265119047619047
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.4250624637672487
+      value: 0.33802241129302657
       name: Cosine Map@100
 ---
 
@@ -269,9 +270,9 @@ from sentence_transformers import SentenceTransformer
 model = SentenceTransformer("redis/model-b-structured")
 # Run inference
 sentences = [
-    'What is the difference between economic growth and economic development?',
-    'What is the difference between economic growth and economic development?',
-    'the difference between economic growth and economic development is What?',
+    'I am poor, ugly, untalented, 20 years old, and have big dreams. How can I succeed in life?',
+    'I am poor, ugly, untalented, 20 years old, and have big dreams. How can I succeed in life?',
+    'Am I too old to succeed in my life at age 32?',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -280,9 +281,9 @@ print(embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[ 1.0000,  1.0000, -0.0482],
-#         [ 1.0000,  1.0000, -0.0482],
-#         [-0.0482, -0.0482,  1.0000]])
+# tensor([[1.0000, 1.0000, 0.5088],
+#         [1.0000, 1.0000, 0.5088],
+#         [0.5088, 0.5088, 1.0000]])
 ```
 
 <!--
@@ -318,23 +319,23 @@ You can finetune this model on your own dataset.
 * Datasets: `NanoMSMARCO` and `NanoNQ`
 * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
 
-| Metric              | NanoMSMARCO | NanoNQ    |
-|:--------------------|:------------|:----------|
-| cosine_accuracy@1   | 0.26        | 0.32      |
-| cosine_accuracy@3   | 0.52        | 0.5       |
-| cosine_accuracy@5   | 0.6         | 0.6       |
-| cosine_accuracy@10  | 0.62        | 0.62      |
-| cosine_precision@1  | 0.26        | 0.32      |
-| cosine_precision@3  | 0.1733      | 0.1733    |
-| cosine_precision@5  | 0.12        | 0.128     |
-| cosine_precision@10 | 0.062       | 0.066     |
-| cosine_recall@1     | 0.26        | 0.3       |
-| cosine_recall@3     | 0.52        | 0.47      |
-| cosine_recall@5     | 0.6         | 0.58      |
-| cosine_recall@10    | 0.62        | 0.6       |
-| **cosine_ndcg@10**  | **0.459**   | **0.462** |
-| cosine_mrr@10       | 0.4052      | 0.4272    |
-| cosine_map@100      | 0.426       | 0.4241    |
+| Metric              | NanoMSMARCO | NanoNQ     |
+|:--------------------|:------------|:-----------|
+| cosine_accuracy@1   | 0.28        | 0.24       |
+| cosine_accuracy@3   | 0.38        | 0.32       |
+| cosine_accuracy@5   | 0.42        | 0.38       |
+| cosine_accuracy@10  | 0.56        | 0.44       |
+| cosine_precision@1  | 0.28        | 0.24       |
+| cosine_precision@3  | 0.1267      | 0.1067     |
+| cosine_precision@5  | 0.084       | 0.076      |
+| cosine_precision@10 | 0.056       | 0.046      |
+| cosine_recall@1     | 0.28        | 0.23       |
+| cosine_recall@3     | 0.38        | 0.3        |
+| cosine_recall@5     | 0.42        | 0.35       |
+| cosine_recall@10    | 0.56        | 0.42       |
+| **cosine_ndcg@10**  | **0.4001**  | **0.3227** |
+| cosine_mrr@10       | 0.3517      | 0.3013     |
+| cosine_map@100      | 0.3734      | 0.3027     |
 
 #### Nano BEIR
 
@@ -352,21 +353,21 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| cosine_accuracy@1   | 0.29       |
-| cosine_accuracy@3   | 0.51       |
-| cosine_accuracy@5   | 0.6        |
-| cosine_accuracy@10  | 0.62       |
-| cosine_precision@1  | 0.29       |
-| cosine_precision@3  | 0.1733     |
-| cosine_precision@5  | 0.124      |
-| cosine_precision@10 | 0.064      |
-| cosine_recall@1     | 0.28       |
-| cosine_recall@3     | 0.495      |
-| cosine_recall@5     | 0.59       |
-| cosine_recall@10    | 0.61       |
-| **cosine_ndcg@10**  | **0.4605** |
-| cosine_mrr@10       | 0.4162     |
-| cosine_map@100      | 0.4251     |
+| cosine_accuracy@1   | 0.26       |
+| cosine_accuracy@3   | 0.35       |
+| cosine_accuracy@5   | 0.4        |
+| cosine_accuracy@10  | 0.5        |
+| cosine_precision@1  | 0.26       |
+| cosine_precision@3  | 0.1167     |
+| cosine_precision@5  | 0.08       |
+| cosine_precision@10 | 0.051      |
+| cosine_recall@1     | 0.255      |
+| cosine_recall@3     | 0.34       |
+| cosine_recall@5     | 0.385      |
+| cosine_recall@10    | 0.49       |
+| **cosine_ndcg@10**  | **0.3614** |
+| cosine_mrr@10       | 0.3265     |
+| cosine_map@100      | 0.338      |
 
 <!--
 ## Bias, Risks and Limitations
@@ -386,19 +387,19 @@ You can finetune this model on your own dataset.
 
 #### Unnamed Dataset
 
-* Size: 713,743 training samples
+* Size: 111,468 training samples
 * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
 * Approximate statistics based on the first 1000 samples:
   |         | anchor                                                                            | positive                                                                          | negative                                                                          |
   |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
   | type    | string                                                                            | string                                                                            | string                                                                            |
-  | details | <ul><li>min: 6 tokens</li><li>mean: 16.07 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.03 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.81 tokens</li><li>max: 58 tokens</li></ul> |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 16.11 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.16 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 17.35 tokens</li><li>max: 76 tokens</li></ul> |
 * Samples:
-  | anchor                                                                         | positive                                                                              | negative                                                                                                          |
-  |:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|
-  | <code>Which one is better Linux OS? Ubuntu or Mint?</code>                     | <code>Why do you use Linux Mint?</code>                                               | <code>Which one is not better Linux OS ? Ubuntu or Mint ?</code>                                                  |
-  | <code>What is flow?</code>                                                     | <code>What is flow?</code>                                                            | <code>What are flow lines?</code>                                                                                 |
-  | <code>How is Trump planning to get Mexico to pay for his supposed wall?</code> | <code>How is it possible for Donald Trump to force Mexico to pay for the wall?</code> | <code>Why do we connect the positive terminal before the negative terminal to ground in a vehicle battery?</code> |
+  | anchor                                                                                | positive                                                                              | negative                                                                                 |
+  |:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|
+  | <code>How many grams of protein should I eat a day?</code>                            | <code>How much protein should I eat per day?</code>                                   | <code>How does hypokalemia lead to polyuria in primary aldosteronism?</code>             |
+  | <code>Who said to get out of economic crisis we need to buy more?</code>              | <code>Who said to get out of economic crisis we need to buy more?</code>              | <code>What are some good IT certifications that don't require programming skills?</code> |
+  | <code>What is the difference between Chinese and western culture within China?</code> | <code>What is the difference between Chinese and western culture within China?</code> | <code>What is the difference between Chinese and western culture outside China?</code>   |
 * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
   ```json
   {
@@ -412,19 +413,19 @@ You can finetune this model on your own dataset.
 
 #### Unnamed Dataset
 
-* Size: 40,000 evaluation samples
+* Size: 12,386 evaluation samples
 * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
 * Approximate statistics based on the first 1000 samples:
   |         | anchor                                                                            | positive                                                                          | negative                                                                          |
   |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
   | type    | string                                                                            | string                                                                            | string                                                                            |
-  | details | <ul><li>min: 6 tokens</li><li>mean: 15.52 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.51 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.79 tokens</li><li>max: 69 tokens</li></ul> |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 16.22 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.28 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 17.39 tokens</li><li>max: 66 tokens</li></ul> |
 * Samples:
-  | anchor                                                                                                                                                       | positive                                                                                                                                                     | negative                                                                                                                                                            |
-  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-  | <code>Why are all my questions on Quora marked needing improvement?</code>                                                                                   | <code>Why are all my questions immediately being marked as needing improvement?</code>                                                                       | <code>For a post-graduate student in IIT, is it allowed to take an external scholarship as a top-up to his/her MHRD assistantship?</code>                           |
-  | <code>Can blue butter fly needle with vaccum tube be reused? Is it HIV risk? . Heard the needle is too small to be reused . Had blood draw at clinic?</code> | <code>Can blue butter fly needle with vaccum tube be reused? Is it HIV risk? . Heard the needle is too small to be reused . Had blood draw at clinic?</code> | <code>Can blue butter fly needle with vaccum tube be reused not ? Is it HIV risk ? . Heard the needle is too small to be reused . Had blood draw at clinic ?</code> |
-  | <code>Why do people still believe the world is flat?</code>                                                                                                  | <code>Why are there still people who believe the world is flat?</code>                                                                                       | <code>I'm not able to buy Udemy course .it is not accepting mine and my friends debit card.my card can be used for Flipkart .how to purchase now?</code>            |
+  | anchor                                                                                                                              | positive                                                                                                                            | negative                                                                     |
+  |:------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|
+  | <code>What is it about novels that allow them to deal with deep themes that short stories, drama, and poetry cannot achieve?</code> | <code>What is it about novels that allow them to deal with deep themes that short stories, drama, and poetry cannot achieve?</code> | <code>What are films that deal with themes like death and letting go?</code> |
+  | <code>If alien civilizations are thought to be much more advanced than us, why haven't they made contact with us yet?</code>        | <code>If there are super intelligent alien beings somewhere in the Galaxy why haven't they tried to contact us yet?</code>          | <code>What's not so good about Aston Martin cars?</code>                     |
+  | <code>How can you determine the Lewis dot structure for sulfur trioxide?</code>                                                     | <code>How can you determine the Lewis dot structure for sulfur trioxide?</code>                                                     | <code>How can you determine the Lewis dot structure for sulfur?</code>       |
 * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
   ```json
   {
@@ -442,7 +443,7 @@ You can finetune this model on your own dataset.
 - `per_device_eval_batch_size`: 128
 - `learning_rate`: 2e-05
 - `weight_decay`: 0.0001
-- `max_steps`: 5000
+- `max_steps`: 3000
 - `warmup_ratio`: 0.1
 - `fp16`: True
 - `dataloader_drop_last`: True
@@ -476,7 +477,7 @@ You can finetune this model on your own dataset.
 - `adam_epsilon`: 1e-08
 - `max_grad_norm`: 1.0
 - `num_train_epochs`: 3.0
-- `max_steps`: 5000
+- `max_steps`: 3000
 - `lr_scheduler_type`: linear
 - `lr_scheduler_kwargs`: {}
 - `warmup_ratio`: 0.1
@@ -583,27 +584,19 @@ You can finetune this model on your own dataset.
 ### Training Logs
 | Epoch  | Step | Training Loss | Validation Loss | NanoMSMARCO_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
 |:------:|:----:|:-------------:|:---------------:|:--------------------------:|:---------------------:|:----------------------------:|
-| 0      | 0    | -             | 0.7908          | 0.5540                     | 0.5931                | 0.5735                       |
-| 0.0448 | 250  | 0.7632        | 0.4756          | 0.5373                     | 0.5302                | 0.5337                       |
-| 0.0897 | 500  | 0.5825        | 0.4308          | 0.5277                     | 0.4949                | 0.5113                       |
-| 0.1345 | 750  | 0.5438        | 0.4161          | 0.5180                     | 0.5039                | 0.5110                       |
-| 0.1793 | 1000 | 0.5277        | 0.4070          | 0.5008                     | 0.4875                | 0.4942                       |
-| 0.2242 | 1250 | 0.516         | 0.4012          | 0.4983                     | 0.4779                | 0.4881                       |
-| 0.2690 | 1500 | 0.5049        | 0.3962          | 0.4923                     | 0.4777                | 0.4850                       |
-| 0.3138 | 1750 | 0.4966        | 0.3931          | 0.4789                     | 0.4769                | 0.4779                       |
-| 0.3587 | 2000 | 0.493         | 0.3894          | 0.4792                     | 0.4616                | 0.4704                       |
-| 0.4035 | 2250 | 0.4852        | 0.3866          | 0.4828                     | 0.4749                | 0.4788                       |
-| 0.4484 | 2500 | 0.4815        | 0.3841          | 0.4589                     | 0.4559                | 0.4574                       |
-| 0.4932 | 2750 | 0.4761        | 0.3820          | 0.4647                     | 0.4539                | 0.4593                       |
-| 0.5380 | 3000 | 0.4747        | 0.3796          | 0.4588                     | 0.4493                | 0.4540                       |
-| 0.5829 | 3250 | 0.4722        | 0.3786          | 0.4588                     | 0.4458                | 0.4523                       |
-| 0.6277 | 3500 | 0.4725        | 0.3774          | 0.4587                     | 0.4537                | 0.4562                       |
-| 0.6725 | 3750 | 0.4692        | 0.3766          | 0.4561                     | 0.4621                | 0.4591                       |
-| 0.7174 | 4000 | 0.4664        | 0.3763          | 0.4584                     | 0.4395                | 0.4489                       |
-| 0.7622 | 4250 | 0.4659        | 0.3747          | 0.4645                     | 0.4586                | 0.4616                       |
-| 0.8070 | 4500 | 0.464         | 0.3742          | 0.4619                     | 0.4479                | 0.4549                       |
-| 0.8519 | 4750 | 0.4662        | 0.3739          | 0.4590                     | 0.4498                | 0.4544                       |
-| 0.8967 | 5000 | 0.4662        | 0.3739          | 0.4590                     | 0.4620                | 0.4605                       |
+| 0      | 0    | -             | 0.5694          | 0.5540                     | 0.5931                | 0.5735                       |
+| 0.2874 | 250  | 0.6309        | 0.4347          | 0.5265                     | 0.5258                | 0.5261                       |
+| 0.5747 | 500  | 0.5501        | 0.4159          | 0.5106                     | 0.4177                | 0.4641                       |
+| 0.8621 | 750  | 0.5266        | 0.4058          | 0.4710                     | 0.3872                | 0.4291                       |
+| 1.1494 | 1000 | 0.5128        | 0.4009          | 0.4510                     | 0.3696                | 0.4103                       |
+| 1.4368 | 1250 | 0.5012        | 0.3967          | 0.4555                     | 0.3549                | 0.4052                       |
+| 1.7241 | 1500 | 0.4973        | 0.3939          | 0.4370                     | 0.3621                | 0.3996                       |
+| 2.0115 | 1750 | 0.4937        | 0.3920          | 0.4131                     | 0.3396                | 0.3763                       |
+| 2.2989 | 2000 | 0.4865        | 0.3902          | 0.4214                     | 0.3226                | 0.3720                       |
+| 2.5862 | 2250 | 0.4844        | 0.3893          | 0.4021                     | 0.3364                | 0.3693                       |
+| 2.8736 | 2500 | 0.4791        | 0.3880          | 0.4090                     | 0.3225                | 0.3657                       |
+| 3.1609 | 2750 | 0.4784        | 0.3874          | 0.4071                     | 0.3233                | 0.3652                       |
+| 3.4483 | 3000 | 0.4758        | 0.3873          | 0.4001                     | 0.3227                | 0.3614                       |
 
 
 ### Framework Versions