Add new SentenceTransformer model

README.md

@@ -5,38 +5,110 @@ tags:
 - feature-extraction
 - dense
 - generated_from_trainer
-- dataset_size:100000
+- dataset_size:359997
 - loss:MultipleNegativesRankingLoss
 base_model: prajjwal1/bert-small
 widget:
-- source_sentence: How do I polish my English skills?
+- source_sentence: When do you use Ms. or Mrs.? Is one for a married woman and one
+    for one that's not married? Which one is for what?
   sentences:
-  - How can we polish English skills?
-  - Why should I move to Israel as a Jew?
-  - What are vitamins responsible for?
-- source_sentence: Can I use the Kozuka Gothic Pro font as a font-face on my web site?
+  - When do you use Ms. or Mrs.? Is one for a married woman and one for one that's
+    not married? Which one is for what?
+  - Nations that do/does otherwise? Which one do I use?
+  - Why don't bikes have a gear indicator?
+- source_sentence: Which ointment is applied to the face of UFC fighters at the commencement
+    of a bout? What does it do?
   sentences:
-  - Can I use the Kozuka Gothic Pro font as a font-face on my web site?
-  - Why are Google, Facebook, YouTube and other social networking sites banned in
-    China?
-  - What font is used in Bloomberg Terminal?
-- source_sentence: Is Quora the best Q&A site?
+  - How can I save a Snapchat video that others posted?
+  - Which ointment is applied to the face of UFC fighters at the commencement of a
+    bout? What does it do?
+  - How do I get the body of a UFC Fighter?
+- source_sentence: Do you love the life you live?
   sentences:
-  - What was the best Quora question ever?
-  - Is Quora the best inquiry site?
-  - Where do I buy Oway hair products online?
-- source_sentence: How can I customize my walking speed on Google Maps?
+  - How do I die and make it look like an accident?
+  - Do you love the life you're living?
+  - Why am I not motivated by money?
+- source_sentence: Ordered food on Swiggy 3 days ago.After accepting my money, said
+    no more on Menu! When if ever will I atleast get refund in cr card a/c?
   sentences:
-  - How do I bring back Google maps icon in my home screen?
-  - How many pages are there in all the Harry Potter books combined?
-  - How can I customize my walking speed on Google Maps?
-- source_sentence: DId something exist before the Big Bang?
+  - Is getting to the Tel Aviv airport to catch a 5:30 AM flight very expensive?
+  - What is a cheap healthy diet I can keep the same and eat every day?
+  - Ordered food on Swiggy 3 days ago.After accepting my money, said no more on Menu!
+    When if ever will I atleast get refund in cr card a/c?
+- source_sentence: How do you earn money on Quora?
   sentences:
-  - How can I improve my memory problem?
-  - Where can I buy Fairy Tail Manga?
-  - Is there a scientific name for what existed before the Big Bang?
+  - What should I do to make my life interesting?
+  - What are the best blogs/sites to follow on fashion?
+  - What is the best way to make money on Quora?
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_ndcg@10
+- cosine_mrr@1
+- cosine_mrr@5
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: SentenceTransformer based on prajjwal1/bert-small
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: val
+      type: val
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.8292
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.905075
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.932925
+      name: Cosine Accuracy@5
+    - type: cosine_precision@1
+      value: 0.8292
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3016916666666666
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.18658500000000003
+      name: Cosine Precision@5
+    - type: cosine_recall@1
+      value: 0.8292
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.905075
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.932925
+      name: Cosine Recall@5
+    - type: cosine_ndcg@10
+      value: 0.895673602678825
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@1
+      value: 0.8292
+      name: Cosine Mrr@1
+    - type: cosine_mrr@5
+      value: 0.869192916666662
+      name: Cosine Mrr@5
+    - type: cosine_mrr@10
+      value: 0.8735491567460258
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8756171762848609
+      name: Cosine Map@100
 ---
 
 # SentenceTransformer based on prajjwal1/bert-small
@@ -85,12 +157,12 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("sentence_transformers_model_id")
+model = SentenceTransformer("redis/model-a-baseline")
 # Run inference
 sentences = [
-    'DId something exist before the Big Bang?',
-    'Is there a scientific name for what existed before the Big Bang?',
-    'Where can I buy Fairy Tail Manga?',
+    'How do you earn money on Quora?',
+    'What is the best way to make money on Quora?',
+    'What are the best blogs/sites to follow on fashion?',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -99,9 +171,9 @@ print(embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[ 1.0000,  0.7596, -0.0398],
-#         [ 0.7596,  1.0000, -0.0308],
-#         [-0.0398, -0.0308,  1.0000]])
+# tensor([[ 1.0000,  0.8451, -0.1292],
+#         [ 0.8451,  1.0000, -0.0389],
+#         [-0.1292, -0.0389,  1.0000]])
 ```
 
 <!--
@@ -128,6 +200,32 @@ You can finetune this model on your own dataset.
 *List how the model may foreseeably be misused and address what users ought not to do with the model.*
 -->
 
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Dataset: `val`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| cosine_accuracy@1  | 0.8292     |
+| cosine_accuracy@3  | 0.9051     |
+| cosine_accuracy@5  | 0.9329     |
+| cosine_precision@1 | 0.8292     |
+| cosine_precision@3 | 0.3017     |
+| cosine_precision@5 | 0.1866     |
+| cosine_recall@1    | 0.8292     |
+| cosine_recall@3    | 0.9051     |
+| cosine_recall@5    | 0.9329     |
+| **cosine_ndcg@10** | **0.8957** |
+| cosine_mrr@1       | 0.8292     |
+| cosine_mrr@5       | 0.8692     |
+| cosine_mrr@10      | 0.8735     |
+| cosine_map@100     | 0.8756     |
+
 <!--
 ## Bias, Risks and Limitations
 
@@ -146,19 +244,45 @@ You can finetune this model on your own dataset.
 
 #### Unnamed Dataset
 
-* Size: 100,000 training samples
-* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
+* Size: 359,997 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: 4 tokens</li><li>mean: 15.46 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.52 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 17.1 tokens</li><li>max: 128 tokens</li></ul> |
+* Samples:
+  | anchor                                                                          | positive                                                                                             | negative                                                                          |
+  |:--------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | <code>Shall I upgrade my iPhone 5s to iOS 10 final version?</code>              | <code>Should I upgrade an iPhone 5s to iOS 10?</code>                                                | <code>What are the disadvantages and advantages of presidential democracy?</code> |
+  | <code>Is Donald Trump really going to be the president of United States?</code> | <code>Do you think Donald Trump could conceivably be the next President of the United States?</code> | <code>What should we do when we are bored?</code>                                 |
+  | <code>What are real tips to improve work life balance?</code>                   | <code>What are the best ways to create a work life balance?</code>                                   | <code>What are the best sites for college students to earn money online?</code>   |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 40,000 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
 * Approximate statistics based on the first 1000 samples:
-  |         | sentence_0                                                                        | sentence_1                                                                       | sentence_2                                                                         |
-  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
-  | type    | string                                                                            | string                                                                           | string                                                                             |
-  | details | <ul><li>min: 3 tokens</li><li>mean: 15.53 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.5 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.87 tokens</li><li>max: 128 tokens</li></ul> |
+  |         | anchor                                                                            | positive                                                                          | negative                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                           |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 15.71 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.79 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.8 tokens</li><li>max: 78 tokens</li></ul> |
 * Samples:
-  | sentence_0                                                                              | sentence_1                                                                              | sentence_2                                                             |
-  |:----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|
-  | <code>Is there visitor entry facility in Jaipur airport. How much is the ticket?</code> | <code>Is there visitor entry facility in Jaipur airport. How much is the ticket?</code> | <code>How much is the airport tax in bogota?</code>                    |
-  | <code>Which concept is more important: good planning or hard work?</code>               | <code>Which concept is more important: good planning or hard work?</code>               | <code>What is important in life: luck or hard work?</code>             |
-  | <code>What is the most efficient way to make money?</code>                              | <code>How can I make my money make money?</code>                                        | <code>What can one learn about Quantum Mechanics in 10 minutes?</code> |
+  | anchor                                                                                                            | positive                                                                                                          | negative                                                                                                                                  |
+  |:------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Why were feathered dinosaur fossils only found in the last 20 years?</code>                                 | <code>Why were feathered dinosaur fossils only found in the last 20 years?</code>                                 | <code>Why are only few people aware that many dinosaurs had feathers?</code>                                                              |
+  | <code>If FOX News is the conservative news station, which cable news network is for liberals/progressives?</code> | <code>If FOX News is the conservative news station, which cable news network is for liberals/progressives?</code> | <code>How much did Fox News and conservative leaning media networks stoke the anger that contributed to Donald Trump's popularity?</code> |
+  | <code>How can guys last longer during sex?</code>                                                                 | <code>How do I last longer in sex?</code>                                                                         | <code>How do you get over the fear of death while fighting a war?</code>                                                                  |
 * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
   ```json
   {
@@ -171,36 +295,49 @@ You can finetune this model on your own dataset.
 ### Training Hyperparameters
 #### Non-Default Hyperparameters
 
-- `per_device_train_batch_size`: 64
-- `per_device_eval_batch_size`: 64
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.001
+- `max_steps`: 14060
+- `warmup_ratio`: 0.1
 - `fp16`: True
-- `multi_dataset_batch_sampler`: round_robin
+- `dataloader_drop_last`: True
+- `dataloader_num_workers`: 1
+- `dataloader_prefetch_factor`: 1
+- `load_best_model_at_end`: True
+- `optim`: adamw_torch
+- `ddp_find_unused_parameters`: False
+- `push_to_hub`: True
+- `hub_model_id`: redis/model-a-baseline
+- `eval_on_start`: True
 
 #### All Hyperparameters
 <details><summary>Click to expand</summary>
 
 - `overwrite_output_dir`: False
 - `do_predict`: False
-- `eval_strategy`: no
+- `eval_strategy`: steps
 - `prediction_loss_only`: True
-- `per_device_train_batch_size`: 64
-- `per_device_eval_batch_size`: 64
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
 - `per_gpu_train_batch_size`: None
 - `per_gpu_eval_batch_size`: None
 - `gradient_accumulation_steps`: 1
 - `eval_accumulation_steps`: None
 - `torch_empty_cache_steps`: None
-- `learning_rate`: 5e-05
-- `weight_decay`: 0.0
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.001
 - `adam_beta1`: 0.9
 - `adam_beta2`: 0.999
 - `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1
-- `num_train_epochs`: 3
-- `max_steps`: -1
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 3.0
+- `max_steps`: 14060
 - `lr_scheduler_type`: linear
 - `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.0
+- `warmup_ratio`: 0.1
 - `warmup_steps`: 0
 - `log_level`: passive
 - `log_level_replica`: warning
@@ -228,14 +365,14 @@ You can finetune this model on your own dataset.
 - `tpu_num_cores`: None
 - `tpu_metrics_debug`: False
 - `debug`: []
-- `dataloader_drop_last`: False
-- `dataloader_num_workers`: 0
-- `dataloader_prefetch_factor`: None
+- `dataloader_drop_last`: True
+- `dataloader_num_workers`: 1
+- `dataloader_prefetch_factor`: 1
 - `past_index`: -1
 - `disable_tqdm`: False
 - `remove_unused_columns`: True
 - `label_names`: None
-- `load_best_model_at_end`: False
+- `load_best_model_at_end`: True
 - `ignore_data_skip`: False
 - `fsdp`: []
 - `fsdp_min_num_params`: 0
@@ -245,23 +382,23 @@ You can finetune this model on your own dataset.
 - `parallelism_config`: None
 - `deepspeed`: None
 - `label_smoothing_factor`: 0.0
-- `optim`: adamw_torch_fused
+- `optim`: adamw_torch
 - `optim_args`: None
 - `adafactor`: False
 - `group_by_length`: False
 - `length_column_name`: length
 - `project`: huggingface
 - `trackio_space_id`: trackio
-- `ddp_find_unused_parameters`: None
+- `ddp_find_unused_parameters`: False
 - `ddp_bucket_cap_mb`: None
 - `ddp_broadcast_buffers`: False
 - `dataloader_pin_memory`: True
 - `dataloader_persistent_workers`: False
 - `skip_memory_metrics`: True
 - `use_legacy_prediction_loop`: False
-- `push_to_hub`: False
+- `push_to_hub`: True
 - `resume_from_checkpoint`: None
-- `hub_model_id`: None
+- `hub_model_id`: redis/model-a-baseline
 - `hub_strategy`: every_save
 - `hub_private_repo`: None
 - `hub_always_push`: False
@@ -288,32 +425,167 @@ You can finetune this model on your own dataset.
 - `neftune_noise_alpha`: None
 - `optim_target_modules`: None
 - `batch_eval_metrics`: False
-- `eval_on_start`: False
+- `eval_on_start`: True
 - `use_liger_kernel`: False
 - `liger_kernel_config`: None
 - `eval_use_gather_object`: False
 - `average_tokens_across_devices`: True
 - `prompts`: None
 - `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: round_robin
+- `multi_dataset_batch_sampler`: proportional
 - `router_mapping`: {}
 - `learning_rate_mapping`: {}
 
 </details>
 
 ### Training Logs
-| Epoch  | Step | Training Loss |
-|:------:|:----:|:-------------:|
-| 0.3199 | 500  | 0.2284        |
-| 0.6398 | 1000 | 0.0571        |
-| 0.9597 | 1500 | 0.0486        |
-| 1.2796 | 2000 | 0.0378        |
-| 1.5995 | 2500 | 0.0367        |
-| 1.9194 | 3000 | 0.0338        |
-| 2.2393 | 3500 | 0.0327        |
-| 2.5592 | 4000 | 0.0285        |
-| 2.8791 | 4500 | 0.0285        |
+<details><summary>Click to expand</summary>
 
+| Epoch  | Step  | Training Loss | Validation Loss | val_cosine_ndcg@10 |
+|:------:|:-----:|:-------------:|:---------------:|:------------------:|
+| 0      | 0     | -             | 1.6259          | 0.8045             |
+| 0.0711 | 100   | 1.9587        | 0.3153          | 0.8459             |
+| 0.1422 | 200   | 0.3989        | 0.1195          | 0.8675             |
+| 0.2134 | 300   | 0.1957        | 0.0920          | 0.8737             |
+| 0.2845 | 400   | 0.1621        | 0.0806          | 0.8766             |
+| 0.3556 | 500   | 0.1425        | 0.0733          | 0.8785             |
+| 0.4267 | 600   | 0.1248        | 0.0688          | 0.8799             |
+| 0.4979 | 700   | 0.117         | 0.0646          | 0.8813             |
+| 0.5690 | 800   | 0.1145        | 0.0616          | 0.8822             |
+| 0.6401 | 900   | 0.1069        | 0.0586          | 0.8832             |
+| 0.7112 | 1000  | 0.101         | 0.0571          | 0.8836             |
+| 0.7824 | 1100  | 0.0981        | 0.0552          | 0.8841             |
+| 0.8535 | 1200  | 0.0938        | 0.0538          | 0.8847             |
+| 0.9246 | 1300  | 0.0941        | 0.0518          | 0.8856             |
+| 0.9957 | 1400  | 0.0869        | 0.0505          | 0.8858             |
+| 1.0669 | 1500  | 0.0811        | 0.0489          | 0.8865             |
+| 1.1380 | 1600  | 0.0767        | 0.0480          | 0.8866             |
+| 1.2091 | 1700  | 0.0745        | 0.0469          | 0.8869             |
+| 1.2802 | 1800  | 0.0722        | 0.0466          | 0.8873             |
+| 1.3514 | 1900  | 0.073         | 0.0452          | 0.8881             |
+| 1.4225 | 2000  | 0.0712        | 0.0450          | 0.8879             |
+| 1.4936 | 2100  | 0.067         | 0.0444          | 0.8882             |
+| 1.5647 | 2200  | 0.0699        | 0.0440          | 0.8883             |
+| 1.6358 | 2300  | 0.0662        | 0.0432          | 0.8885             |
+| 1.7070 | 2400  | 0.0697        | 0.0421          | 0.8894             |
+| 1.7781 | 2500  | 0.0685        | 0.0418          | 0.8892             |
+| 1.8492 | 2600  | 0.0649        | 0.0408          | 0.8896             |
+| 1.9203 | 2700  | 0.0673        | 0.0406          | 0.8895             |
+| 1.9915 | 2800  | 0.065         | 0.0403          | 0.8898             |
+| 2.0626 | 2900  | 0.0601        | 0.0398          | 0.8903             |
+| 2.1337 | 3000  | 0.0583        | 0.0394          | 0.8904             |
+| 2.2048 | 3100  | 0.0557        | 0.0388          | 0.8905             |
+| 2.2760 | 3200  | 0.0588        | 0.0389          | 0.8908             |
+| 2.3471 | 3300  | 0.0587        | 0.0386          | 0.8908             |
+| 2.4182 | 3400  | 0.0564        | 0.0384          | 0.8907             |
+| 2.4893 | 3500  | 0.0567        | 0.0384          | 0.8907             |
+| 2.5605 | 3600  | 0.0562        | 0.0381          | 0.8910             |
+| 2.6316 | 3700  | 0.0532        | 0.0375          | 0.8912             |
+| 2.7027 | 3800  | 0.0522        | 0.0375          | 0.8912             |
+| 2.7738 | 3900  | 0.0555        | 0.0377          | 0.8914             |
+| 2.8450 | 4000  | 0.0518        | 0.0371          | 0.8916             |
+| 2.9161 | 4100  | 0.0529        | 0.0368          | 0.8920             |
+| 2.9872 | 4200  | 0.0561        | 0.0367          | 0.8921             |
+| 3.0583 | 4300  | 0.052         | 0.0365          | 0.8921             |
+| 3.1294 | 4400  | 0.0515        | 0.0362          | 0.8924             |
+| 3.2006 | 4500  | 0.0518        | 0.0357          | 0.8926             |
+| 3.2717 | 4600  | 0.0522        | 0.0358          | 0.8927             |
+| 3.3428 | 4700  | 0.0524        | 0.0357          | 0.8926             |
+| 3.4139 | 4800  | 0.0472        | 0.0355          | 0.8926             |
+| 3.4851 | 4900  | 0.0518        | 0.0354          | 0.8929             |
+| 3.5562 | 5000  | 0.0497        | 0.0352          | 0.8926             |
+| 3.6273 | 5100  | 0.0502        | 0.0349          | 0.8929             |
+| 3.6984 | 5200  | 0.0478        | 0.0349          | 0.8929             |
+| 3.7696 | 5300  | 0.0449        | 0.0346          | 0.8936             |
+| 3.8407 | 5400  | 0.0506        | 0.0347          | 0.8930             |
+| 3.9118 | 5500  | 0.0502        | 0.0347          | 0.8931             |
+| 3.9829 | 5600  | 0.0501        | 0.0343          | 0.8932             |
+| 4.0541 | 5700  | 0.0476        | 0.0343          | 0.8936             |
+| 4.1252 | 5800  | 0.046         | 0.0340          | 0.8937             |
+| 4.1963 | 5900  | 0.0479        | 0.0342          | 0.8937             |
+| 4.2674 | 6000  | 0.0436        | 0.0339          | 0.8939             |
+| 4.3385 | 6100  | 0.046         | 0.0338          | 0.8936             |
+| 4.4097 | 6200  | 0.0474        | 0.0335          | 0.8939             |
+| 4.4808 | 6300  | 0.0452        | 0.0335          | 0.8938             |
+| 4.5519 | 6400  | 0.043         | 0.0333          | 0.8942             |
+| 4.6230 | 6500  | 0.044         | 0.0333          | 0.8944             |
+| 4.6942 | 6600  | 0.0472        | 0.0331          | 0.8944             |
+| 4.7653 | 6700  | 0.0426        | 0.0330          | 0.8943             |
+| 4.8364 | 6800  | 0.0459        | 0.0331          | 0.8943             |
+| 4.9075 | 6900  | 0.0465        | 0.0331          | 0.8945             |
+| 4.9787 | 7000  | 0.0427        | 0.0328          | 0.8943             |
+| 5.0498 | 7100  | 0.0395        | 0.0328          | 0.8940             |
+| 5.1209 | 7200  | 0.0409        | 0.0326          | 0.8942             |
+| 5.1920 | 7300  | 0.0423        | 0.0326          | 0.8943             |
+| 5.2632 | 7400  | 0.0433        | 0.0327          | 0.8940             |
+| 5.3343 | 7500  | 0.0434        | 0.0324          | 0.8944             |
+| 5.4054 | 7600  | 0.0428        | 0.0324          | 0.8945             |
+| 5.4765 | 7700  | 0.0423        | 0.0323          | 0.8945             |
+| 5.5477 | 7800  | 0.0426        | 0.0323          | 0.8946             |
+| 5.6188 | 7900  | 0.0425        | 0.0322          | 0.8947             |
+| 5.6899 | 8000  | 0.0428        | 0.0322          | 0.8949             |
+| 5.7610 | 8100  | 0.0427        | 0.0319          | 0.8950             |
+| 5.8321 | 8200  | 0.0412        | 0.0323          | 0.8949             |
+| 5.9033 | 8300  | 0.0424        | 0.0321          | 0.8950             |
+| 5.9744 | 8400  | 0.0402        | 0.0322          | 0.8949             |
+| 6.0455 | 8500  | 0.0418        | 0.0319          | 0.8950             |
+| 6.1166 | 8600  | 0.0391        | 0.0318          | 0.8952             |
+| 6.1878 | 8700  | 0.0409        | 0.0317          | 0.8948             |
+| 6.2589 | 8800  | 0.0386        | 0.0316          | 0.8949             |
+| 6.3300 | 8900  | 0.0401        | 0.0318          | 0.8950             |
+| 6.4011 | 9000  | 0.0413        | 0.0317          | 0.8950             |
+| 6.4723 | 9100  | 0.0392        | 0.0315          | 0.8951             |
+| 6.5434 | 9200  | 0.0418        | 0.0317          | 0.8947             |
+| 6.6145 | 9300  | 0.0416        | 0.0316          | 0.8949             |
+| 6.6856 | 9400  | 0.0394        | 0.0315          | 0.8948             |
+| 6.7568 | 9500  | 0.0388        | 0.0314          | 0.8949             |
+| 6.8279 | 9600  | 0.0389        | 0.0313          | 0.8951             |
+| 6.8990 | 9700  | 0.0409        | 0.0314          | 0.8952             |
+| 6.9701 | 9800  | 0.043         | 0.0312          | 0.8953             |
+| 7.0413 | 9900  | 0.04          | 0.0313          | 0.8952             |
+| 7.1124 | 10000 | 0.0384        | 0.0313          | 0.8951             |
+| 7.1835 | 10100 | 0.0402        | 0.0313          | 0.8951             |
+| 7.2546 | 10200 | 0.04          | 0.0312          | 0.8955             |
+| 7.3257 | 10300 | 0.0378        | 0.0311          | 0.8953             |
+| 7.3969 | 10400 | 0.0377        | 0.0310          | 0.8954             |
+| 7.4680 | 10500 | 0.0381        | 0.0310          | 0.8955             |
+| 7.5391 | 10600 | 0.0378        | 0.0310          | 0.8955             |
+| 7.6102 | 10700 | 0.0381        | 0.0311          | 0.8953             |
+| 7.6814 | 10800 | 0.0379        | 0.0310          | 0.8955             |
+| 7.7525 | 10900 | 0.0409        | 0.0311          | 0.8952             |
+| 7.8236 | 11000 | 0.0402        | 0.0309          | 0.8957             |
+| 7.8947 | 11100 | 0.0381        | 0.0308          | 0.8954             |
+| 7.9659 | 11200 | 0.0378        | 0.0308          | 0.8954             |
+| 8.0370 | 11300 | 0.0404        | 0.0309          | 0.8955             |
+| 8.1081 | 11400 | 0.0373        | 0.0308          | 0.8957             |
+| 8.1792 | 11500 | 0.0365        | 0.0308          | 0.8955             |
+| 8.2504 | 11600 | 0.0355        | 0.0308          | 0.8954             |
+| 8.3215 | 11700 | 0.0395        | 0.0307          | 0.8952             |
+| 8.3926 | 11800 | 0.0389        | 0.0307          | 0.8953             |
+| 8.4637 | 11900 | 0.0383        | 0.0308          | 0.8952             |
+| 8.5349 | 12000 | 0.036         | 0.0307          | 0.8954             |
+| 8.6060 | 12100 | 0.0388        | 0.0307          | 0.8955             |
+| 8.6771 | 12200 | 0.0356        | 0.0307          | 0.8955             |
+| 8.7482 | 12300 | 0.0379        | 0.0306          | 0.8957             |
+| 8.8193 | 12400 | 0.0379        | 0.0306          | 0.8956             |
+| 8.8905 | 12500 | 0.0366        | 0.0305          | 0.8956             |
+| 8.9616 | 12600 | 0.038         | 0.0305          | 0.8957             |
+| 9.0327 | 12700 | 0.0378        | 0.0305          | 0.8957             |
+| 9.1038 | 12800 | 0.0359        | 0.0306          | 0.8956             |
+| 9.1750 | 12900 | 0.0385        | 0.0305          | 0.8955             |
+| 9.2461 | 13000 | 0.0374        | 0.0305          | 0.8956             |
+| 9.3172 | 13100 | 0.0396        | 0.0305          | 0.8956             |
+| 9.3883 | 13200 | 0.0379        | 0.0305          | 0.8956             |
+| 9.4595 | 13300 | 0.0366        | 0.0305          | 0.8957             |
+| 9.5306 | 13400 | 0.0378        | 0.0305          | 0.8956             |
+| 9.6017 | 13500 | 0.0363        | 0.0305          | 0.8956             |
+| 9.6728 | 13600 | 0.0372        | 0.0305          | 0.8956             |
+| 9.7440 | 13700 | 0.0405        | 0.0305          | 0.8957             |
+| 9.8151 | 13800 | 0.039         | 0.0305          | 0.8957             |
+| 9.8862 | 13900 | 0.0375        | 0.0304          | 0.8956             |
+| 9.9573 | 14000 | 0.0396        | 0.0304          | 0.8957             |
+
+</details>
 
 ### Framework Versions
 - Python: 3.10.18