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: 0.761525
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.826125
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.85095
+      name: Cosine Accuracy@5
+    - type: cosine_precision@1
+      value: 0.761525
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.275375
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17019000000000004
+      name: Cosine Precision@5
+    - type: cosine_recall@1
+      value: 0.761525
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.826125
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.85095
+      name: Cosine Recall@5
+    - type: cosine_ndcg@10
+      value: 0.8202534934281767
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@1
+      value: 0.761525
+      name: Cosine Mrr@1
+    - type: cosine_mrr@5
+      value: 0.7961479166666627
+      name: Cosine Mrr@5
+    - type: cosine_mrr@10
+      value: 0.8004402281746008
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8038638243708912
+      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([[ 1.0001,  0.9996, -0.3001],
+#         [ 0.9996,  1.0000, -0.3007],
+#         [-0.3001, -0.3007,  1.0003]])
 ```
 
 <!--
@@ -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  | 0.7615     |
+| cosine_accuracy@3  | 0.8261     |
+| cosine_accuracy@5  | 0.8509     |
+| cosine_precision@1 | 0.7615     |
+| cosine_precision@3 | 0.2754     |
+| cosine_precision@5 | 0.1702     |
+| cosine_recall@1    | 0.7615     |
+| cosine_recall@3    | 0.8261     |
+| cosine_recall@5    | 0.8509     |
+| **cosine_ndcg@10** | **0.8203** |
+| cosine_mrr@1       | 0.7615     |
+| cosine_mrr@5       | 0.7961     |
+| cosine_mrr@10      | 0.8004     |
+| cosine_map@100     | 0.8039     |
+
 <!--
 ## Bias, Risks and Limitations
 
@@ -146,23 +244,49 @@ 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:
-  |         | sentence_0                                                                        | sentence_1                                                                        | sentence_2                                                                        |
+  |         | 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": 1.0,
+      "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:
+  |         | 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
   {
-      "scale": 20.0,
+      "scale": 1.0,
       "similarity_fct": "cos_sim",
       "gather_across_devices": false
   }
@@ -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     | -             | 5.9531          | 0.7603             |
+| 0.0711 | 100   | 5.9694        | 5.7072          | 0.7792             |
+| 0.1422 | 200   | 5.7181        | 5.4263          | 0.7865             |
+| 0.2134 | 300   | 5.5628        | 5.3443          | 0.7829             |
+| 0.2845 | 400   | 5.4947        | 5.3221          | 0.7774             |
+| 0.3556 | 500   | 5.4597        | 5.3180          | 0.7741             |
+| 0.4267 | 600   | 5.4387        | 5.3158          | 0.7737             |
+| 0.4979 | 700   | 5.423         | 5.3141          | 0.7751             |
+| 0.5690 | 800   | 5.4108        | 5.3109          | 0.7848             |
+| 0.6401 | 900   | 5.397         | 5.2923          | 0.8008             |
+| 0.7112 | 1000  | 5.3724        | 5.2839          | 0.8004             |
+| 0.7824 | 1100  | 5.3625        | 5.2804          | 0.8007             |
+| 0.8535 | 1200  | 5.355         | 5.2777          | 0.8013             |
+| 0.9246 | 1300  | 5.3499        | 5.2748          | 0.8030             |
+| 0.9957 | 1400  | 5.3442        | 5.2729          | 0.8067             |
+| 1.0669 | 1500  | 5.3382        | 5.2624          | 0.8103             |
+| 1.1380 | 1600  | 5.3254        | 5.2557          | 0.8138             |
+| 1.2091 | 1700  | 5.3159        | 5.2441          | 0.8163             |
+| 1.2802 | 1800  | 5.3035        | 5.2350          | 0.8180             |
+| 1.3514 | 1900  | 5.295         | 5.2303          | 0.8179             |
+| 1.4225 | 2000  | 5.2925        | 5.2292          | 0.8182             |
+| 1.4936 | 2100  | 5.2881        | 5.2271          | 0.8187             |
+| 1.5647 | 2200  | 5.2854        | 5.2258          | 0.8187             |
+| 1.6358 | 2300  | 5.2831        | 5.2258          | 0.8189             |
+| 1.7070 | 2400  | 5.2805        | 5.2247          | 0.8192             |
+| 1.7781 | 2500  | 5.278         | 5.2247          | 0.8186             |
+| 1.8492 | 2600  | 5.2761        | 5.2230          | 0.8184             |
+| 1.9203 | 2700  | 5.2754        | 5.2221          | 0.8185             |
+| 1.9915 | 2800  | 5.274         | 5.2228          | 0.8185             |
+| 2.0626 | 2900  | 5.2722        | 5.2209          | 0.8175             |
+| 2.1337 | 3000  | 5.2708        | 5.2206          | 0.8182             |
+| 2.2048 | 3100  | 5.2686        | 5.2211          | 0.8194             |
+| 2.2760 | 3200  | 5.2666        | 5.2204          | 0.8186             |
+| 2.3471 | 3300  | 5.2671        | 5.2192          | 0.8191             |
+| 2.4182 | 3400  | 5.2657        | 5.2200          | 0.8188             |
+| 2.4893 | 3500  | 5.2638        | 5.2188          | 0.8184             |
+| 2.5605 | 3600  | 5.2635        | 5.2189          | 0.8188             |
+| 2.6316 | 3700  | 5.2624        | 5.2187          | 0.8192             |
+| 2.7027 | 3800  | 5.262         | 5.2178          | 0.8182             |
+| 2.7738 | 3900  | 5.2608        | 5.2175          | 0.8188             |
+| 2.8450 | 4000  | 5.2595        | 5.2179          | 0.8189             |
+| 2.9161 | 4100  | 5.2599        | 5.2163          | 0.8191             |
+| 2.9872 | 4200  | 5.2587        | 5.2162          | 0.8184             |
+| 3.0583 | 4300  | 5.2574        | 5.2168          | 0.8193             |
+| 3.1294 | 4400  | 5.256         | 5.2165          | 0.8197             |
+| 3.2006 | 4500  | 5.2551        | 5.2158          | 0.8188             |
+| 3.2717 | 4600  | 5.2552        | 5.2159          | 0.8188             |
+| 3.3428 | 4700  | 5.2549        | 5.2157          | 0.8192             |
+| 3.4139 | 4800  | 5.2531        | 5.2154          | 0.8192             |
+| 3.4851 | 4900  | 5.2534        | 5.2152          | 0.8191             |
+| 3.5562 | 5000  | 5.2528        | 5.2146          | 0.8197             |
+| 3.6273 | 5100  | 5.2521        | 5.2149          | 0.8193             |
+| 3.6984 | 5200  | 5.2509        | 5.2145          | 0.8199             |
+| 3.7696 | 5300  | 5.2509        | 5.2144          | 0.8189             |
+| 3.8407 | 5400  | 5.2495        | 5.2139          | 0.8195             |
+| 3.9118 | 5500  | 5.2496        | 5.2140          | 0.8195             |
+| 3.9829 | 5600  | 5.2505        | 5.2135          | 0.8193             |
+| 4.0541 | 5700  | 5.2478        | 5.2140          | 0.8197             |
+| 4.1252 | 5800  | 5.2476        | 5.2136          | 0.8196             |
+| 4.1963 | 5900  | 5.248         | 5.2130          | 0.8199             |
+| 4.2674 | 6000  | 5.2482        | 5.2129          | 0.8196             |
+| 4.3385 | 6100  | 5.2466        | 5.2135          | 0.8196             |
+| 4.4097 | 6200  | 5.2461        | 5.2126          | 0.8196             |
+| 4.4808 | 6300  | 5.2453        | 5.2124          | 0.8196             |
+| 4.5519 | 6400  | 5.2448        | 5.2128          | 0.8197             |
+| 4.6230 | 6500  | 5.2439        | 5.2124          | 0.8193             |
+| 4.6942 | 6600  | 5.244         | 5.2123          | 0.8192             |
+| 4.7653 | 6700  | 5.2428        | 5.2114          | 0.8192             |
+| 4.8364 | 6800  | 5.2433        | 5.2112          | 0.8197             |
+| 4.9075 | 6900  | 5.2439        | 5.2117          | 0.8194             |
+| 4.9787 | 7000  | 5.2422        | 5.2121          | 0.8204             |
+| 5.0498 | 7100  | 5.2425        | 5.2114          | 0.8198             |
+| 5.1209 | 7200  | 5.2418        | 5.2113          | 0.8201             |
+| 5.1920 | 7300  | 5.2416        | 5.2113          | 0.8200             |
+| 5.2632 | 7400  | 5.2405        | 5.2109          | 0.8199             |
+| 5.3343 | 7500  | 5.242         | 5.2106          | 0.8197             |
+| 5.4054 | 7600  | 5.2402        | 5.2105          | 0.8199             |
+| 5.4765 | 7700  | 5.2393        | 5.2108          | 0.8203             |
+| 5.5477 | 7800  | 5.24          | 5.2104          | 0.8198             |
+| 5.6188 | 7900  | 5.2395        | 5.2103          | 0.8201             |
+| 5.6899 | 8000  | 5.2381        | 5.2102          | 0.8198             |
+| 5.7610 | 8100  | 5.2399        | 5.2102          | 0.8195             |
+| 5.8321 | 8200  | 5.2395        | 5.2100          | 0.8195             |
+| 5.9033 | 8300  | 5.2377        | 5.2100          | 0.8197             |
+| 5.9744 | 8400  | 5.238         | 5.2097          | 0.8198             |
+| 6.0455 | 8500  | 5.2372        | 5.2097          | 0.8200             |
+| 6.1166 | 8600  | 5.2368        | 5.2095          | 0.8200             |
+| 6.1878 | 8700  | 5.2378        | 5.2096          | 0.8201             |
+| 6.2589 | 8800  | 5.2372        | 5.2097          | 0.8197             |
+| 6.3300 | 8900  | 5.2365        | 5.2098          | 0.8197             |
+| 6.4011 | 9000  | 5.2367        | 5.2092          | 0.8199             |
+| 6.4723 | 9100  | 5.2364        | 5.2093          | 0.8197             |
+| 6.5434 | 9200  | 5.2362        | 5.2095          | 0.8202             |
+| 6.6145 | 9300  | 5.2359        | 5.2096          | 0.8199             |
+| 6.6856 | 9400  | 5.2345        | 5.2095          | 0.8203             |
+| 6.7568 | 9500  | 5.2362        | 5.2090          | 0.8202             |
+| 6.8279 | 9600  | 5.2353        | 5.2089          | 0.8201             |
+| 6.8990 | 9700  | 5.2346        | 5.2090          | 0.8203             |
+| 6.9701 | 9800  | 5.2354        | 5.2090          | 0.8202             |
+| 7.0413 | 9900  | 5.234         | 5.2089          | 0.8202             |
+| 7.1124 | 10000 | 5.2334        | 5.2087          | 0.8202             |
+| 7.1835 | 10100 | 5.2342        | 5.2089          | 0.8204             |
+| 7.2546 | 10200 | 5.2342        | 5.2089          | 0.8204             |
+| 7.3257 | 10300 | 5.2336        | 5.2085          | 0.8203             |
+| 7.3969 | 10400 | 5.2347        | 5.2086          | 0.8206             |
+| 7.4680 | 10500 | 5.2326        | 5.2086          | 0.8203             |
+| 7.5391 | 10600 | 5.2336        | 5.2082          | 0.8201             |
+| 7.6102 | 10700 | 5.2328        | 5.2084          | 0.8202             |
+| 7.6814 | 10800 | 5.2328        | 5.2085          | 0.8203             |
+| 7.7525 | 10900 | 5.2321        | 5.2083          | 0.8201             |
+| 7.8236 | 11000 | 5.2332        | 5.2082          | 0.8202             |
+| 7.8947 | 11100 | 5.2325        | 5.2082          | 0.8202             |
+| 7.9659 | 11200 | 5.2331        | 5.2082          | 0.8200             |
+| 8.0370 | 11300 | 5.2322        | 5.2081          | 0.8202             |
+| 8.1081 | 11400 | 5.2324        | 5.2082          | 0.8206             |
+| 8.1792 | 11500 | 5.2318        | 5.2080          | 0.8200             |
+| 8.2504 | 11600 | 5.2314        | 5.2082          | 0.8202             |
+| 8.3215 | 11700 | 5.2318        | 5.2082          | 0.8202             |
+| 8.3926 | 11800 | 5.2317        | 5.2078          | 0.8203             |
+| 8.4637 | 11900 | 5.2312        | 5.2078          | 0.8202             |
+| 8.5349 | 12000 | 5.2327        | 5.2079          | 0.8201             |
+| 8.6060 | 12100 | 5.2316        | 5.2077          | 0.8203             |
+| 8.6771 | 12200 | 5.2317        | 5.2078          | 0.8204             |
+| 8.7482 | 12300 | 5.2301        | 5.2079          | 0.8202             |
+| 8.8193 | 12400 | 5.2308        | 5.2077          | 0.8201             |
+| 8.8905 | 12500 | 5.2306        | 5.2078          | 0.8200             |
+| 8.9616 | 12600 | 5.231         | 5.2077          | 0.8200             |
+| 9.0327 | 12700 | 5.2307        | 5.2076          | 0.8199             |
+| 9.1038 | 12800 | 5.2309        | 5.2076          | 0.8201             |
+| 9.1750 | 12900 | 5.2301        | 5.2076          | 0.8200             |
+| 9.2461 | 13000 | 5.231         | 5.2076          | 0.8202             |
+| 9.3172 | 13100 | 5.2312        | 5.2075          | 0.8201             |
+| 9.3883 | 13200 | 5.2304        | 5.2077          | 0.8204             |
+| 9.4595 | 13300 | 5.2304        | 5.2075          | 0.8202             |
+| 9.5306 | 13400 | 5.2312        | 5.2076          | 0.8203             |
+| 9.6017 | 13500 | 5.2304        | 5.2076          | 0.8204             |
+| 9.6728 | 13600 | 5.2309        | 5.2076          | 0.8203             |
+| 9.7440 | 13700 | 5.23          | 5.2075          | 0.8202             |
+| 9.8151 | 13800 | 5.2301        | 5.2075          | 0.8201             |
+| 9.8862 | 13900 | 5.231         | 5.2075          | 0.8203             |
+| 9.9573 | 14000 | 5.2303        | 5.2075          | 0.8203             |
+
+</details>
 
 ### Framework Versions
 - Python: 3.10.18