Add new SentenceTransformer model

README.md

@@ -5,38 +5,114 @@ 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 calculate IQ?
+- 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:
-  - What is the easiest way to know my IQ?
-  - How do I calculate not IQ ?
-  - What are some creative and innovative business ideas with less investment in India?
-- source_sentence: How can I learn martial arts in my home?
+  - 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?
+  - What is the best way to make money on Quora?
+- source_sentence: Which ointment is applied to the face of UFC fighters at the commencement
+    of a bout? What does it do?
   sentences:
-  - How can I learn martial arts by myself?
-  - What are the advantages and disadvantages of investing in gold?
-  - Can people see that I have looked at their pictures on instagram if I am not following
-    them?
-- source_sentence: When Enterprise picks you up do you have to take them back?
+  - Why don't bikes have a gear indicator?
+  - 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:
-  - Are there any software Training institute in Tuticorin?
-  - When Enterprise picks you up do you have to take them back?
-  - When Enterprise picks you up do them have to take youback?
-- source_sentence: What are some non-capital goods?
+  - Which file formats are compatible with iTunes?
+  - Do you love the life you're living?
+  - What is the best way to find a person just using their phone by trying to track
+    the other persons phone and get a location from it?
+- source_sentence: Can I do shoulder and triceps workout on same day? What other combinations
+    like this can I do?
   sentences:
-  - What are capital goods?
-  - How is the value of [math]\pi[/math] calculated?
-  - What are some non-capital goods?
-- source_sentence: What is the QuickBooks technical support phone number in New York?
+  - Can I do shoulder and triceps workout on same day? I can What other combinations
+    like thisdo?
+  - How can I save a Snapchat video that others posted?
+  - Can I do shoulder and triceps workout on same day? What other combinations like
+    this can I do?
+- source_sentence: I am a married woman and I'm in love with married man. what should
+    I do?
   sentences:
-  - What caused the Great Depression?
-  - Can I apply for PR in Canada?
-  - Which is the best QuickBooks Hosting Support Number in New York?
+  - How can I earn money easily online?
+  - I am not a married woman and I 'm in love with married man . what should I do
+    ?
+  - I am a married woman and I'm in love with married man. what should I do?
 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: 0.828025
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9027
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.931025
+      name: Cosine Accuracy@5
+    - type: cosine_precision@1
+      value: 0.828025
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3008999999999999
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.186205
+      name: Cosine Precision@5
+    - type: cosine_recall@1
+      value: 0.828025
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9027
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.931025
+      name: Cosine Recall@5
+    - type: cosine_ndcg@10
+      value: 0.8942284691055087
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@1
+      value: 0.828025
+      name: Cosine Mrr@1
+    - type: cosine_mrr@5
+      value: 0.8677179166666629
+      name: Cosine Mrr@5
+    - type: cosine_mrr@10
+      value: 0.8721162896825339
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8742240723304836
+      name: Cosine Map@100
 ---
 
 # SentenceTransformer based on prajjwal1/bert-small
@@ -85,12 +161,12 @@ 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-b-structured")
 # Run inference
 sentences = [
-    'What is the QuickBooks technical support phone number in New York?',
-    'Which is the best QuickBooks Hosting Support Number in New York?',
-    'Can I apply for PR in Canada?',
+    "I am a married woman and I'm in love with married man. what should I do?",
+    "I am a married woman and I'm in love with married man. what should I do?",
+    "I am not a married woman and I 'm in love with married man . what should I do ?",
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -99,9 +175,9 @@ print(embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[1.0000, 0.8563, 0.0594],
-#         [0.8563, 1.0000, 0.1245],
-#         [0.0594, 0.1245, 1.0000]])
+# tensor([[1.0000, 1.0000, 0.4050],
+#         [1.0000, 1.0000, 0.4050],
+#         [0.4050, 0.4050, 1.0000]])
 ```
 
 <!--
@@ -128,6 +204,32 @@ 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  | 0.828      |
+| cosine_accuracy@3  | 0.9027     |
+| cosine_accuracy@5  | 0.931      |
+| cosine_precision@1 | 0.828      |
+| cosine_precision@3 | 0.3009     |
+| cosine_precision@5 | 0.1862     |
+| cosine_recall@1    | 0.828      |
+| cosine_recall@3    | 0.9027     |
+| cosine_recall@5    | 0.931      |
+| **cosine_ndcg@10** | **0.8942** |
+| cosine_mrr@1       | 0.828      |
+| cosine_mrr@5       | 0.8677     |
+| cosine_mrr@10      | 0.8721     |
+| cosine_map@100     | 0.8742     |
+
 <!--
 ## Bias, Risks and Limitations
 
@@ -146,19 +248,45 @@ 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: 4 tokens</li><li>mean: 16.63 tokens</li><li>max: 59 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>Shall my iPhone 5s upgrade Ito iOS 10 final version?</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>Is Donald Trump really going not to be the president of United States ?</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>How far is Miami from Fort Lauderdale?</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                                                                        |
+  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
   |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
   | type    | string                                                                            | string                                                                            | string                                                                            |
-  | details | <ul><li>min: 6 tokens</li><li>mean: 15.79 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.68 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 16.37 tokens</li><li>max: 67 tokens</li></ul> |
+  | 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: 5 tokens</li><li>mean: 16.59 tokens</li><li>max: 77 tokens</li></ul> |
 * Samples:
-  | sentence_0                                                       | sentence_1                                                       | sentence_2                                                                        |
-  |:-----------------------------------------------------------------|:-----------------------------------------------------------------|:----------------------------------------------------------------------------------|
-  | <code>Is masturbating bad for boys?</code>                       | <code>Is masturbating bad for boys?</code>                       | <code>How harmful or unhealthy is masturbation?</code>                            |
-  | <code>Does a train engine move in reverse?</code>                | <code>Does a train engine move in reverse?</code>                | <code>Time moves forward, not in reverse. Doesn't that make time a vector?</code> |
-  | <code>What is the most badass thing anyone has ever done?</code> | <code>What is the most badass thing anyone has ever done?</code> | <code>anyone is the most badass thing Whathas ever done?</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>Why does economics require calculus?</code>                                                                                         |
 * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
   ```json
   {
@@ -171,36 +299,49 @@ 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-b-structured
+- `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 +369,14 @@ 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 +386,23 @@ 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-b-structured
 - `hub_strategy`: every_save
 - `hub_private_repo`: None
 - `hub_always_push`: False
@@ -288,32 +429,167 @@ 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  | 0.4294        |
-| 0.6398 | 1000 | 0.1268        |
-| 0.9597 | 1500 | 0.1           |
-| 1.2796 | 2000 | 0.0792        |
-| 1.5995 | 2500 | 0.0706        |
-| 1.9194 | 3000 | 0.0687        |
-| 2.2393 | 3500 | 0.0584        |
-| 2.5592 | 4000 | 0.057         |
-| 2.8791 | 4500 | 0.0581        |
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss | Validation Loss | val_cosine_ndcg@10 |
+|:------:|:-----:|:-------------:|:---------------:|:------------------:|
+| 0      | 0     | -             | 1.7418          | 0.7821             |
+| 0.0711 | 100   | 2.0777        | 0.7932          | 0.8130             |
+| 0.1422 | 200   | 0.7966        | 0.4005          | 0.8510             |
+| 0.2134 | 300   | 0.3991        | 0.2603          | 0.8615             |
+| 0.2845 | 400   | 0.3153        | 0.2051          | 0.8652             |
+| 0.3556 | 500   | 0.2593        | 0.1740          | 0.8681             |
+| 0.4267 | 600   | 0.2231        | 0.1568          | 0.8707             |
+| 0.4979 | 700   | 0.2017        | 0.1443          | 0.8727             |
+| 0.5690 | 800   | 0.1933        | 0.1322          | 0.8746             |
+| 0.6401 | 900   | 0.1818        | 0.1217          | 0.8755             |
+| 0.7112 | 1000  | 0.1714        | 0.1141          | 0.8769             |
+| 0.7824 | 1100  | 0.157         | 0.1060          | 0.8780             |
+| 0.8535 | 1200  | 0.1467        | 0.0998          | 0.8788             |
+| 0.9246 | 1300  | 0.1394        | 0.0937          | 0.8805             |
+| 0.9957 | 1400  | 0.1343        | 0.0910          | 0.8813             |
+| 1.0669 | 1500  | 0.1222        | 0.0853          | 0.8822             |
+| 1.1380 | 1600  | 0.1173        | 0.0820          | 0.8821             |
+| 1.2091 | 1700  | 0.1082        | 0.0797          | 0.8828             |
+| 1.2802 | 1800  | 0.1105        | 0.0777          | 0.8835             |
+| 1.3514 | 1900  | 0.1093        | 0.0734          | 0.8833             |
+| 1.4225 | 2000  | 0.1034        | 0.0744          | 0.8840             |
+| 1.4936 | 2100  | 0.1016        | 0.0713          | 0.8845             |
+| 1.5647 | 2200  | 0.0995        | 0.0699          | 0.8851             |
+| 1.6358 | 2300  | 0.0994        | 0.0679          | 0.8849             |
+| 1.7070 | 2400  | 0.1024        | 0.0667          | 0.8867             |
+| 1.7781 | 2500  | 0.0911        | 0.0658          | 0.8868             |
+| 1.8492 | 2600  | 0.0907        | 0.0640          | 0.8861             |
+| 1.9203 | 2700  | 0.0941        | 0.0632          | 0.8859             |
+| 1.9915 | 2800  | 0.093         | 0.0625          | 0.8870             |
+| 2.0626 | 2900  | 0.0814        | 0.0618          | 0.8875             |
+| 2.1337 | 3000  | 0.0811        | 0.0609          | 0.8868             |
+| 2.2048 | 3100  | 0.0773        | 0.0602          | 0.8880             |
+| 2.2760 | 3200  | 0.0813        | 0.0590          | 0.8873             |
+| 2.3471 | 3300  | 0.0806        | 0.0584          | 0.8876             |
+| 2.4182 | 3400  | 0.0765        | 0.0575          | 0.8882             |
+| 2.4893 | 3500  | 0.0774        | 0.0581          | 0.8889             |
+| 2.5605 | 3600  | 0.0761        | 0.0560          | 0.8883             |
+| 2.6316 | 3700  | 0.0735        | 0.0560          | 0.8886             |
+| 2.7027 | 3800  | 0.0711        | 0.0555          | 0.8891             |
+| 2.7738 | 3900  | 0.0747        | 0.0551          | 0.8889             |
+| 2.8450 | 4000  | 0.0731        | 0.0552          | 0.8897             |
+| 2.9161 | 4100  | 0.0708        | 0.0543          | 0.8898             |
+| 2.9872 | 4200  | 0.0778        | 0.0536          | 0.8901             |
+| 3.0583 | 4300  | 0.0697        | 0.0540          | 0.8893             |
+| 3.1294 | 4400  | 0.0668        | 0.0533          | 0.8900             |
+| 3.2006 | 4500  | 0.0679        | 0.0526          | 0.8893             |
+| 3.2717 | 4600  | 0.0652        | 0.0532          | 0.8902             |
+| 3.3428 | 4700  | 0.0673        | 0.0520          | 0.8899             |
+| 3.4139 | 4800  | 0.0625        | 0.0514          | 0.8903             |
+| 3.4851 | 4900  | 0.0669        | 0.0515          | 0.8912             |
+| 3.5562 | 5000  | 0.0641        | 0.0515          | 0.8915             |
+| 3.6273 | 5100  | 0.0637        | 0.0509          | 0.8909             |
+| 3.6984 | 5200  | 0.0635        | 0.0506          | 0.8908             |
+| 3.7696 | 5300  | 0.0606        | 0.0499          | 0.8915             |
+| 3.8407 | 5400  | 0.0633        | 0.0503          | 0.8917             |
+| 3.9118 | 5500  | 0.0656        | 0.0498          | 0.8913             |
+| 3.9829 | 5600  | 0.0658        | 0.0492          | 0.8916             |
+| 4.0541 | 5700  | 0.0606        | 0.0489          | 0.8917             |
+| 4.1252 | 5800  | 0.0585        | 0.0485          | 0.8914             |
+| 4.1963 | 5900  | 0.0613        | 0.0490          | 0.8914             |
+| 4.2674 | 6000  | 0.0568        | 0.0487          | 0.8909             |
+| 4.3385 | 6100  | 0.0576        | 0.0481          | 0.8918             |
+| 4.4097 | 6200  | 0.0603        | 0.0481          | 0.8915             |
+| 4.4808 | 6300  | 0.0569        | 0.0480          | 0.8918             |
+| 4.5519 | 6400  | 0.0553        | 0.0477          | 0.8921             |
+| 4.6230 | 6500  | 0.057         | 0.0472          | 0.8918             |
+| 4.6942 | 6600  | 0.0602        | 0.0472          | 0.8925             |
+| 4.7653 | 6700  | 0.0541        | 0.0468          | 0.8922             |
+| 4.8364 | 6800  | 0.0588        | 0.0468          | 0.8917             |
+| 4.9075 | 6900  | 0.0588        | 0.0471          | 0.8920             |
+| 4.9787 | 7000  | 0.0549        | 0.0469          | 0.8921             |
+| 5.0498 | 7100  | 0.0522        | 0.0466          | 0.8920             |
+| 5.1209 | 7200  | 0.0527        | 0.0462          | 0.8924             |
+| 5.1920 | 7300  | 0.0519        | 0.0461          | 0.8924             |
+| 5.2632 | 7400  | 0.0544        | 0.0459          | 0.8927             |
+| 5.3343 | 7500  | 0.0549        | 0.0456          | 0.8925             |
+| 5.4054 | 7600  | 0.0527        | 0.0460          | 0.8932             |
+| 5.4765 | 7700  | 0.0519        | 0.0453          | 0.8920             |
+| 5.5477 | 7800  | 0.0528        | 0.0455          | 0.8928             |
+| 5.6188 | 7900  | 0.0525        | 0.0451          | 0.8929             |
+| 5.6899 | 8000  | 0.0535        | 0.0454          | 0.8931             |
+| 5.7610 | 8100  | 0.0526        | 0.0452          | 0.8931             |
+| 5.8321 | 8200  | 0.0507        | 0.0454          | 0.8930             |
+| 5.9033 | 8300  | 0.0511        | 0.0451          | 0.8932             |
+| 5.9744 | 8400  | 0.0489        | 0.0451          | 0.8930             |
+| 6.0455 | 8500  | 0.0509        | 0.0451          | 0.8929             |
+| 6.1166 | 8600  | 0.0487        | 0.0447          | 0.8931             |
+| 6.1878 | 8700  | 0.0494        | 0.0449          | 0.8932             |
+| 6.2589 | 8800  | 0.0474        | 0.0444          | 0.8932             |
+| 6.3300 | 8900  | 0.049         | 0.0448          | 0.8934             |
+| 6.4011 | 9000  | 0.0492        | 0.0446          | 0.8934             |
+| 6.4723 | 9100  | 0.0493        | 0.0443          | 0.8931             |
+| 6.5434 | 9200  | 0.0517        | 0.0442          | 0.8931             |
+| 6.6145 | 9300  | 0.0502        | 0.0445          | 0.8938             |
+| 6.6856 | 9400  | 0.0501        | 0.0441          | 0.8935             |
+| 6.7568 | 9500  | 0.0484        | 0.0439          | 0.8935             |
+| 6.8279 | 9600  | 0.0472        | 0.0437          | 0.8935             |
+| 6.8990 | 9700  | 0.0484        | 0.0435          | 0.8936             |
+| 6.9701 | 9800  | 0.051         | 0.0433          | 0.8933             |
+| 7.0413 | 9900  | 0.0496        | 0.0435          | 0.8935             |
+| 7.1124 | 10000 | 0.0469        | 0.0434          | 0.8937             |
+| 7.1835 | 10100 | 0.0479        | 0.0432          | 0.8935             |
+| 7.2546 | 10200 | 0.0476        | 0.0430          | 0.8937             |
+| 7.3257 | 10300 | 0.0454        | 0.0431          | 0.8934             |
+| 7.3969 | 10400 | 0.0445        | 0.0430          | 0.8937             |
+| 7.4680 | 10500 | 0.0471        | 0.0427          | 0.8936             |
+| 7.5391 | 10600 | 0.0441        | 0.0429          | 0.8938             |
+| 7.6102 | 10700 | 0.046         | 0.0429          | 0.8932             |
+| 7.6814 | 10800 | 0.046         | 0.0428          | 0.8934             |
+| 7.7525 | 10900 | 0.049         | 0.0428          | 0.8938             |
+| 7.8236 | 11000 | 0.0476        | 0.0427          | 0.8939             |
+| 7.8947 | 11100 | 0.0468        | 0.0425          | 0.8938             |
+| 7.9659 | 11200 | 0.0465        | 0.0426          | 0.8940             |
+| 8.0370 | 11300 | 0.048         | 0.0428          | 0.8938             |
+| 8.1081 | 11400 | 0.0448        | 0.0425          | 0.8937             |
+| 8.1792 | 11500 | 0.0431        | 0.0424          | 0.8939             |
+| 8.2504 | 11600 | 0.0428        | 0.0424          | 0.8935             |
+| 8.3215 | 11700 | 0.046         | 0.0424          | 0.8937             |
+| 8.3926 | 11800 | 0.0471        | 0.0423          | 0.8938             |
+| 8.4637 | 11900 | 0.0466        | 0.0424          | 0.8943             |
+| 8.5349 | 12000 | 0.0431        | 0.0421          | 0.8941             |
+| 8.6060 | 12100 | 0.0462        | 0.0421          | 0.8938             |
+| 8.6771 | 12200 | 0.0425        | 0.0423          | 0.8941             |
+| 8.7482 | 12300 | 0.0455        | 0.0421          | 0.8941             |
+| 8.8193 | 12400 | 0.0445        | 0.0422          | 0.8940             |
+| 8.8905 | 12500 | 0.0455        | 0.0422          | 0.8943             |
+| 8.9616 | 12600 | 0.0448        | 0.0421          | 0.8941             |
+| 9.0327 | 12700 | 0.0462        | 0.0421          | 0.8940             |
+| 9.1038 | 12800 | 0.0429        | 0.0421          | 0.8939             |
+| 9.1750 | 12900 | 0.0452        | 0.0421          | 0.8942             |
+| 9.2461 | 13000 | 0.0439        | 0.0420          | 0.8943             |
+| 9.3172 | 13100 | 0.0472        | 0.0420          | 0.8942             |
+| 9.3883 | 13200 | 0.0447        | 0.0420          | 0.8943             |
+| 9.4595 | 13300 | 0.0426        | 0.0420          | 0.8942             |
+| 9.5306 | 13400 | 0.0445        | 0.0420          | 0.8942             |
+| 9.6017 | 13500 | 0.0436        | 0.0419          | 0.8942             |
+| 9.6728 | 13600 | 0.0445        | 0.0419          | 0.8943             |
+| 9.7440 | 13700 | 0.0477        | 0.0419          | 0.8943             |
+| 9.8151 | 13800 | 0.0439        | 0.0419          | 0.8942             |
+| 9.8862 | 13900 | 0.0438        | 0.0419          | 0.8942             |
+| 9.9573 | 14000 | 0.0468        | 0.0419          | 0.8942             |
 
+</details>
 
 ### Framework Versions
 - Python: 3.10.18