Add new SentenceTransformer model

README.md

@@ -5,38 +5,110 @@ tags:
 - feature-extraction
 - dense
 - generated_from_trainer
-- dataset_size:100000
+- dataset_size:359999
 - loss:MultipleNegativesRankingLoss
 base_model: prajjwal1/bert-small
 widget:
-- source_sentence: How do I calculate IQ?
+- source_sentence: Someone blocked me on Instagram. How do I unblock myself from their
+    account?
   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?
+  - Someone blocked me on Instagram. How do I unblock myself from their account?
+  - Someone blocked me on Instagram. How do myself unblock Ifrom their account?
+  - What are some good tips for dealing with a very easily frustrated 1 year old?
+- source_sentence: Do you love the life you live?
   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?
+  - What is Jakob Nowell, Bradley Nowell's son, up to and will he pursue a career
+    in music?
+  - Do you love the life you're living?
+  - Do you love not the life you live ?
+- source_sentence: I had sex on the 9th and my period started on the 11th. Could I
+    still get pregnant?
   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?
+  - How can I earn money easily online?
+  - If I have sex on the day of my ovulation and I get my period two weeks later,
+    can I still be pregnant?
+  - I did not have sex on the 9th and my period started on the 11th . Could I still
+    get pregnant ?
+- source_sentence: Would you read book at your office?
   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?
+  - Would book read youat your office?
+  - I am a married woman and I'm in love with married man. what should I do?
+  - Would you read book at your office?
+- source_sentence: How do you earn money on Quora?
   sentences:
-  - What caused the Great Depression?
-  - Can I apply for PR in Canada?
-  - Which is the best QuickBooks Hosting Support Number in New York?
+  - 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?
+  - How do you earn not money on Quora ?
+  - 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: 0.82935
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.903025
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9311
+      name: Cosine Accuracy@5
+    - type: cosine_precision@1
+      value: 0.82935
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.30100833333333327
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.18622
+      name: Cosine Precision@5
+    - type: cosine_recall@1
+      value: 0.82935
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.903025
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9311
+      name: Cosine Recall@5
+    - type: cosine_ndcg@10
+      value: 0.8950372962037911
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@1
+      value: 0.82935
+      name: Cosine Mrr@1
+    - type: cosine_mrr@5
+      value: 0.8687558333333282
+      name: Cosine Mrr@5
+    - type: cosine_mrr@10
+      value: 0.8731832242063449
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8752427301346968
+      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-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?',
+    'How do you earn money on Quora?',
+    'What is the best way to make money on Quora?',
+    'How do you earn not money on Quora ?',
 ]
 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, 0.8563, 0.0594],
-#         [0.8563, 1.0000, 0.1245],
-#         [0.0594, 0.1245, 1.0000]])
+# tensor([[1.0000, 0.8575, 0.0777],
+#         [0.8575, 1.0000, 0.0442],
+#         [0.0777, 0.0442, 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  | 0.8294    |
+| cosine_accuracy@3  | 0.903     |
+| cosine_accuracy@5  | 0.9311    |
+| cosine_precision@1 | 0.8294    |
+| cosine_precision@3 | 0.301     |
+| cosine_precision@5 | 0.1862    |
+| cosine_recall@1    | 0.8294    |
+| cosine_recall@3    | 0.903     |
+| cosine_recall@5    | 0.9311    |
+| **cosine_ndcg@10** | **0.895** |
+| cosine_mrr@1       | 0.8294    |
+| cosine_mrr@5       | 0.8688    |
+| cosine_mrr@10      | 0.8732    |
+| cosine_map@100     | 0.8752    |
+
 <!--
 ## 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: 359,999 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: 15.4 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.45 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.07 tokens</li><li>max: 62 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 I upgrade not my iPhone 5s to iOS 10 final version ?</code>                                             |
+  | <code>Do Census Bureau income figures count sources of unearned income, or do they just count earned income?</code> | <code>Do Census Bureau income figures count sources of unearned income, or do they just count earned income?</code> | <code>Do Census Bureau income figures count sources of unearned income, or do income just count earned they?</code> |
+  | <code>Who has the highest IQ?</code>                                                                                | <code>Who has the highest IQ?</code>                                                                                | <code>the highest IQ has Who?</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: 15.86 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.94 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.46 tokens</li><li>max: 66 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>What are some mind-blowing Iphone gadgets and tools that most people don't know about?</code>               | <code>What are some mind-blowing iphone tools that most people don't know about?</code>                           | <code>most people are some mind-blowing Iphone gadgets and tools that Whatdon't know about?</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>If FOX News is not the conservative news station , which cable news network is for liberals / progressives ?</code> |
+  | <code>How can guys last longer during sex?</code>                                                                 | <code>How do I last longer in sex?</code>                                                                         | <code>How can guys last not longer during sex ?</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-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 +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-b-structured
 - `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  | 0.4294        |
-| 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     | -             | 1.8606          | 0.7604             |
+| 0.0711 | 100   | 2.2043        | 1.2529          | 0.7830             |
+| 0.1422 | 200   | 1.0111        | 0.4899          | 0.8464             |
+| 0.2134 | 300   | 0.4916        | 0.3181          | 0.8590             |
+| 0.2845 | 400   | 0.3572        | 0.2448          | 0.8632             |
+| 0.3556 | 500   | 0.2893        | 0.2091          | 0.8670             |
+| 0.4267 | 600   | 0.262         | 0.1866          | 0.8695             |
+| 0.4979 | 700   | 0.2356        | 0.1702          | 0.8720             |
+| 0.5690 | 800   | 0.207         | 0.1551          | 0.8730             |
+| 0.6401 | 900   | 0.1914        | 0.1421          | 0.8745             |
+| 0.7112 | 1000  | 0.185         | 0.1320          | 0.8765             |
+| 0.7824 | 1100  | 0.1663        | 0.1233          | 0.8771             |
+| 0.8535 | 1200  | 0.1521        | 0.1148          | 0.8788             |
+| 0.9246 | 1300  | 0.1482        | 0.1069          | 0.8789             |
+| 0.9957 | 1400  | 0.1385        | 0.1023          | 0.8810             |
+| 1.0669 | 1500  | 0.1298        | 0.0942          | 0.8799             |
+| 1.1380 | 1600  | 0.1239        | 0.0915          | 0.8818             |
+| 1.2091 | 1700  | 0.1197        | 0.0890          | 0.8821             |
+| 1.2802 | 1800  | 0.1123        | 0.0850          | 0.8827             |
+| 1.3514 | 1900  | 0.1004        | 0.0821          | 0.8836             |
+| 1.4225 | 2000  | 0.1089        | 0.0795          | 0.8838             |
+| 1.4936 | 2100  | 0.1044        | 0.0784          | 0.8845             |
+| 1.5647 | 2200  | 0.0963        | 0.0763          | 0.8843             |
+| 1.6358 | 2300  | 0.0962        | 0.0738          | 0.8844             |
+| 1.7070 | 2400  | 0.0987        | 0.0710          | 0.8851             |
+| 1.7781 | 2500  | 0.0942        | 0.0705          | 0.8872             |
+| 1.8492 | 2600  | 0.0914        | 0.0670          | 0.8856             |
+| 1.9203 | 2700  | 0.0899        | 0.0681          | 0.8870             |
+| 1.9915 | 2800  | 0.0918        | 0.0652          | 0.8869             |
+| 2.0626 | 2900  | 0.0744        | 0.0652          | 0.8866             |
+| 2.1337 | 3000  | 0.0791        | 0.0638          | 0.8875             |
+| 2.2048 | 3100  | 0.0752        | 0.0629          | 0.8871             |
+| 2.2760 | 3200  | 0.0751        | 0.0628          | 0.8887             |
+| 2.3471 | 3300  | 0.0727        | 0.0617          | 0.8885             |
+| 2.4182 | 3400  | 0.0741        | 0.0605          | 0.8883             |
+| 2.4893 | 3500  | 0.074         | 0.0603          | 0.8883             |
+| 2.5605 | 3600  | 0.0746        | 0.0594          | 0.8888             |
+| 2.6316 | 3700  | 0.0736        | 0.0587          | 0.8889             |
+| 2.7027 | 3800  | 0.0685        | 0.0571          | 0.8887             |
+| 2.7738 | 3900  | 0.0723        | 0.0567          | 0.8893             |
+| 2.8450 | 4000  | 0.0693        | 0.0556          | 0.8885             |
+| 2.9161 | 4100  | 0.0708        | 0.0554          | 0.8894             |
+| 2.9872 | 4200  | 0.0701        | 0.0554          | 0.8901             |
+| 3.0583 | 4300  | 0.0651        | 0.0551          | 0.8895             |
+| 3.1294 | 4400  | 0.0601        | 0.0546          | 0.8895             |
+| 3.2006 | 4500  | 0.0618        | 0.0539          | 0.8904             |
+| 3.2717 | 4600  | 0.0618        | 0.0536          | 0.8904             |
+| 3.3428 | 4700  | 0.0606        | 0.0535          | 0.8906             |
+| 3.4139 | 4800  | 0.0612        | 0.0532          | 0.8901             |
+| 3.4851 | 4900  | 0.0605        | 0.0526          | 0.8912             |
+| 3.5562 | 5000  | 0.0612        | 0.0523          | 0.8909             |
+| 3.6273 | 5100  | 0.0591        | 0.0515          | 0.8907             |
+| 3.6984 | 5200  | 0.0624        | 0.0510          | 0.8906             |
+| 3.7696 | 5300  | 0.0584        | 0.0518          | 0.8916             |
+| 3.8407 | 5400  | 0.0577        | 0.0506          | 0.8913             |
+| 3.9118 | 5500  | 0.0582        | 0.0506          | 0.8916             |
+| 3.9829 | 5600  | 0.0625        | 0.0505          | 0.8914             |
+| 4.0541 | 5700  | 0.0564        | 0.0500          | 0.8909             |
+| 4.1252 | 5800  | 0.0532        | 0.0496          | 0.8923             |
+| 4.1963 | 5900  | 0.0537        | 0.0492          | 0.8923             |
+| 4.2674 | 6000  | 0.0527        | 0.0493          | 0.8920             |
+| 4.3385 | 6100  | 0.0528        | 0.0490          | 0.8920             |
+| 4.4097 | 6200  | 0.0524        | 0.0495          | 0.8919             |
+| 4.4808 | 6300  | 0.0552        | 0.0484          | 0.8924             |
+| 4.5519 | 6400  | 0.0547        | 0.0490          | 0.8921             |
+| 4.6230 | 6500  | 0.0522        | 0.0481          | 0.8927             |
+| 4.6942 | 6600  | 0.0489        | 0.0486          | 0.8918             |
+| 4.7653 | 6700  | 0.0484        | 0.0484          | 0.8923             |
+| 4.8364 | 6800  | 0.0494        | 0.0482          | 0.8926             |
+| 4.9075 | 6900  | 0.0486        | 0.0479          | 0.8928             |
+| 4.9787 | 7000  | 0.0498        | 0.0474          | 0.8930             |
+| 5.0498 | 7100  | 0.0503        | 0.0475          | 0.8933             |
+| 5.1209 | 7200  | 0.0491        | 0.0472          | 0.8931             |
+| 5.1920 | 7300  | 0.0484        | 0.0471          | 0.8933             |
+| 5.2632 | 7400  | 0.0466        | 0.0467          | 0.8930             |
+| 5.3343 | 7500  | 0.0495        | 0.0468          | 0.8930             |
+| 5.4054 | 7600  | 0.0465        | 0.0467          | 0.8932             |
+| 5.4765 | 7700  | 0.0449        | 0.0462          | 0.8929             |
+| 5.5477 | 7800  | 0.0487        | 0.0461          | 0.8934             |
+| 5.6188 | 7900  | 0.0463        | 0.0460          | 0.8933             |
+| 5.6899 | 8000  | 0.0471        | 0.0457          | 0.8930             |
+| 5.7610 | 8100  | 0.0488        | 0.0458          | 0.8936             |
+| 5.8321 | 8200  | 0.045         | 0.0458          | 0.8932             |
+| 5.9033 | 8300  | 0.0494        | 0.0456          | 0.8937             |
+| 5.9744 | 8400  | 0.044         | 0.0456          | 0.8938             |
+| 6.0455 | 8500  | 0.0442        | 0.0459          | 0.8941             |
+| 6.1166 | 8600  | 0.0453        | 0.0455          | 0.8938             |
+| 6.1878 | 8700  | 0.0443        | 0.0452          | 0.8937             |
+| 6.2589 | 8800  | 0.044         | 0.0448          | 0.8937             |
+| 6.3300 | 8900  | 0.042         | 0.0455          | 0.8942             |
+| 6.4011 | 9000  | 0.0458        | 0.0451          | 0.8941             |
+| 6.4723 | 9100  | 0.0426        | 0.0450          | 0.8939             |
+| 6.5434 | 9200  | 0.0439        | 0.0446          | 0.8939             |
+| 6.6145 | 9300  | 0.0459        | 0.0444          | 0.8944             |
+| 6.6856 | 9400  | 0.0435        | 0.0447          | 0.8943             |
+| 6.7568 | 9500  | 0.0414        | 0.0443          | 0.8942             |
+| 6.8279 | 9600  | 0.0452        | 0.0447          | 0.8942             |
+| 6.8990 | 9700  | 0.044         | 0.0446          | 0.8942             |
+| 6.9701 | 9800  | 0.0447        | 0.0443          | 0.8942             |
+| 7.0413 | 9900  | 0.0431        | 0.0442          | 0.8943             |
+| 7.1124 | 10000 | 0.0414        | 0.0441          | 0.8945             |
+| 7.1835 | 10100 | 0.0409        | 0.0440          | 0.8947             |
+| 7.2546 | 10200 | 0.0455        | 0.0440          | 0.8946             |
+| 7.3257 | 10300 | 0.04          | 0.0438          | 0.8946             |
+| 7.3969 | 10400 | 0.0424        | 0.0437          | 0.8947             |
+| 7.4680 | 10500 | 0.0407        | 0.0438          | 0.8942             |
+| 7.5391 | 10600 | 0.0409        | 0.0435          | 0.8943             |
+| 7.6102 | 10700 | 0.0437        | 0.0434          | 0.8946             |
+| 7.6814 | 10800 | 0.0427        | 0.0435          | 0.8946             |
+| 7.7525 | 10900 | 0.0421        | 0.0434          | 0.8948             |
+| 7.8236 | 11000 | 0.0394        | 0.0432          | 0.8947             |
+| 7.8947 | 11100 | 0.0388        | 0.0434          | 0.8947             |
+| 7.9659 | 11200 | 0.0402        | 0.0432          | 0.8947             |
+| 8.0370 | 11300 | 0.0405        | 0.0431          | 0.8947             |
+| 8.1081 | 11400 | 0.0405        | 0.0432          | 0.8946             |
+| 8.1792 | 11500 | 0.0424        | 0.0433          | 0.8949             |
+| 8.2504 | 11600 | 0.0407        | 0.0432          | 0.8948             |
+| 8.3215 | 11700 | 0.0401        | 0.0430          | 0.8946             |
+| 8.3926 | 11800 | 0.0404        | 0.0429          | 0.8949             |
+| 8.4637 | 11900 | 0.0388        | 0.0428          | 0.8950             |
+| 8.5349 | 12000 | 0.0405        | 0.0427          | 0.8948             |
+| 8.6060 | 12100 | 0.0391        | 0.0427          | 0.8948             |
+| 8.6771 | 12200 | 0.039         | 0.0427          | 0.8948             |
+| 8.7482 | 12300 | 0.0375        | 0.0427          | 0.8948             |
+| 8.8193 | 12400 | 0.0393        | 0.0428          | 0.8948             |
+| 8.8905 | 12500 | 0.0392        | 0.0427          | 0.8949             |
+| 8.9616 | 12600 | 0.0417        | 0.0427          | 0.8951             |
+| 9.0327 | 12700 | 0.0397        | 0.0426          | 0.8951             |
+| 9.1038 | 12800 | 0.0424        | 0.0426          | 0.8949             |
+| 9.1750 | 12900 | 0.0386        | 0.0426          | 0.8948             |
+| 9.2461 | 13000 | 0.0389        | 0.0425          | 0.8950             |
+| 9.3172 | 13100 | 0.0379        | 0.0426          | 0.8950             |
+| 9.3883 | 13200 | 0.04          | 0.0426          | 0.8952             |
+| 9.4595 | 13300 | 0.038         | 0.0425          | 0.8951             |
+| 9.5306 | 13400 | 0.039         | 0.0425          | 0.8950             |
+| 9.6017 | 13500 | 0.0448        | 0.0425          | 0.8950             |
+| 9.6728 | 13600 | 0.0389        | 0.0425          | 0.8951             |
+| 9.7440 | 13700 | 0.0395        | 0.0425          | 0.8951             |
+| 9.8151 | 13800 | 0.0362        | 0.0425          | 0.8951             |
+| 9.8862 | 13900 | 0.037         | 0.0425          | 0.8950             |
+| 9.9573 | 14000 | 0.0399        | 0.0425          | 0.8950             |
+
+</details>
 
 ### Framework Versions
 - Python: 3.10.18