Training in progress, step 14060

Information-Retrieval_evaluation_val_results.csv

@@ -1,2 +1,3 @@
 epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-MRR@1,cosine-MRR@5,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
 -1,-1,0.908,0.9684,0.9834,0.908,0.908,0.3228,0.9684,0.19667999999999997,0.9834,0.908,0.9386633333333337,0.9400269841269848,0.9532296698470627,0.9404621256346036
+-1,-1,0.9104,0.9688,0.9842,0.9104,0.9104,0.32293333333333335,0.9688,0.19683999999999996,0.9842,0.9104,0.9402433333333332,0.9416250793650793,0.9545809774353143,0.9420576026548708

README.md

@@ -5,108 +5,38 @@ tags:
 - feature-extraction
 - dense
 - generated_from_trainer
-- dataset_size:90000
+- dataset_size:100000
 - loss:MultipleNegativesRankingLoss
 base_model: prajjwal1/bert-small
 widget:
-- source_sentence: How do I cope with my depression to keep my girlfriend?
+- source_sentence: How do I calculate IQ?
   sentences:
-  - How do you cope with depression?
-  - How do I cope with my depression to keep my girlfriend?
-  - What is the significance of Nevanlinna theory?
-- source_sentence: Which is the best college for MBA in Delhi?
+  - 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?
   sentences:
-  - Who played the character of 'Russ' in friends?
-  - What will be the picture quality if a Standard STB is Connected to a Full HD 40"
-    Led TV?
-  - Which is the best college to do an MBA in Delhi?
-- source_sentence: What is poison mailbox?
+  - 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?
   sentences:
-  - What are the differences between a concentric and eccentric movement?
-  - What is poison mailbox?
-  - What is not poison mailbox ?
-- source_sentence: What are the examples of a presence of mind?
+  - 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?
   sentences:
-  - What are not the examples of a presence of mind ?
-  - What's the point of dressing well?
-  - What are some of the greatest examples of the presence of mind?
-- source_sentence: What are some tips on making it through the job interview process
-    at Opus Bank?
+  - 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?
   sentences:
-  - I've got an online coupon for Domino's pizza through the freecharge app. Is it
-    necessary to use that coupon only when I order online?
-  - What are not some tips on making it through the job interview process at Opus
-    Bank ?
-  - What are some tips on making it through the job interview process at Opus Bank?
+  - What caused the Great Depression?
+  - Can I apply for PR in Canada?
+  - Which is the best QuickBooks Hosting Support Number in New York?
 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: 0.9104
-      name: Cosine Accuracy@1
-    - type: cosine_accuracy@3
-      value: 0.9688
-      name: Cosine Accuracy@3
-    - type: cosine_accuracy@5
-      value: 0.9842
-      name: Cosine Accuracy@5
-    - type: cosine_precision@1
-      value: 0.9104
-      name: Cosine Precision@1
-    - type: cosine_precision@3
-      value: 0.32293333333333335
-      name: Cosine Precision@3
-    - type: cosine_precision@5
-      value: 0.19683999999999996
-      name: Cosine Precision@5
-    - type: cosine_recall@1
-      value: 0.9104
-      name: Cosine Recall@1
-    - type: cosine_recall@3
-      value: 0.9688
-      name: Cosine Recall@3
-    - type: cosine_recall@5
-      value: 0.9842
-      name: Cosine Recall@5
-    - type: cosine_ndcg@10
-      value: 0.954585167414727
-      name: Cosine Ndcg@10
-    - type: cosine_mrr@1
-      value: 0.9104
-      name: Cosine Mrr@1
-    - type: cosine_mrr@5
-      value: 0.9402533333333333
-      name: Cosine Mrr@5
-    - type: cosine_mrr@10
-      value: 0.9416303174603176
-      name: Cosine Mrr@10
-    - type: cosine_map@100
-      value: 0.9420641228013908
-      name: Cosine Map@100
 ---
 
 # SentenceTransformer based on prajjwal1/bert-small
@@ -155,12 +85,12 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("redis/model-b-structured")
+model = SentenceTransformer("sentence_transformers_model_id")
 # Run inference
 sentences = [
-    'What are some tips on making it through the job interview process at Opus Bank?',
-    'What are some tips on making it through the job interview process at Opus Bank?',
-    'What are not some tips on making it through the job interview process at Opus Bank ?',
+    '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?',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -169,9 +99,9 @@ print(embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[1.0000, 1.0000, 0.1581],
-#         [1.0000, 1.0000, 0.1581],
-#         [0.1581, 0.1581, 1.0000]])
+# tensor([[1.0000, 0.8563, 0.0594],
+#         [0.8563, 1.0000, 0.1245],
+#         [0.0594, 0.1245, 1.0000]])
 ```
 
 <!--
@@ -198,32 +128,6 @@ 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  | 0.9104     |
-| cosine_accuracy@3  | 0.9688     |
-| cosine_accuracy@5  | 0.9842     |
-| cosine_precision@1 | 0.9104     |
-| cosine_precision@3 | 0.3229     |
-| cosine_precision@5 | 0.1968     |
-| cosine_recall@1    | 0.9104     |
-| cosine_recall@3    | 0.9688     |
-| cosine_recall@5    | 0.9842     |
-| **cosine_ndcg@10** | **0.9546** |
-| cosine_mrr@1       | 0.9104     |
-| cosine_mrr@5       | 0.9403     |
-| cosine_mrr@10      | 0.9416     |
-| cosine_map@100     | 0.9421     |
-
 <!--
 ## Bias, Risks and Limitations
 
@@ -242,45 +146,19 @@ You can finetune this model on your own dataset.
 
 #### Unnamed Dataset
 
-* Size: 90,000 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: 15.63 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.77 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.2 tokens</li><li>max: 75 tokens</li></ul> |
-* Samples:
-  | anchor                                                   | positive                                                 | negative                                                                    |
-  |:---------------------------------------------------------|:---------------------------------------------------------|:----------------------------------------------------------------------------|
-  | <code>How long did it take to develop Pokémon GO?</code> | <code>How long did it take to develop Pokémon GO?</code> | <code>Can I take more than one gym in Pokémon GO?</code>                    |
-  | <code>How bad is 6/18 eyesight?</code>                   | <code>How bad is 6/18 eyesight?</code>                   | <code>How was bad eyesight dealt with in ancient and medieval times?</code> |
-  | <code>How can I do learn speaking English easily?</code> | <code>How can I learn speaking English easily?</code>    | <code>How can English do learn speaking Ieasily?</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: 5,000 evaluation samples
-* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Size: 100,000 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
 * Approximate statistics based on the first 1000 samples:
-  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
+  |         | sentence_0                                                                        | sentence_1                                                                        | sentence_2                                                                        |
   |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
   | type    | string                                                                            | string                                                                            | string                                                                            |
-  | details | <ul><li>min: 6 tokens</li><li>mean: 15.65 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.69 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.66 tokens</li><li>max: 55 tokens</li></ul> |
+  | 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> |
 * Samples:
-  | anchor                                                                                                                                                             | positive                                                                                                                                                           | negative                                                                                                                                                               |
-  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-  | <code>What's it like working in IT for Goldman Sachs?</code>                                                                                                       | <code>What's it like working in IT for Goldman Sachs?</code>                                                                                                       | <code>it 's Whatlike working in IT for Goldman Sachs?</code>                                                                                                           |
-  | <code>Will time travel be possible in future?</code>                                                                                                               | <code>Is time travel still theorized as being possible?</code>                                                                                                     | <code>Will time travel be not possible in future ?</code>                                                                                                              |
-  | <code>For creating a software based service for SME’s, we need to tie up with a bank. Need the best way to contact the right person in big banks like HDFC.</code> | <code>For creating a software based service for SME’s, we need to tie up with a bank. Need the best way to contact the right person in big banks like HDFC.</code> | <code>For creating a software based service for SME ’s , we need to tie up with a bank . Need the right way to contact the best person in big banks like HDFC .</code> |
+  | 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>                   |
 * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
   ```json
   {
@@ -293,49 +171,36 @@ You can finetune this model on your own dataset.
 ### Training Hyperparameters
 #### Non-Default Hyperparameters
 
-- `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`: 3510
-- `warmup_ratio`: 0.1
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
 - `fp16`: True
-- `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
+- `multi_dataset_batch_sampler`: round_robin
 
 #### All Hyperparameters
 <details><summary>Click to expand</summary>
 
 - `overwrite_output_dir`: False
 - `do_predict`: False
-- `eval_strategy`: steps
+- `eval_strategy`: no
 - `prediction_loss_only`: True
-- `per_device_train_batch_size`: 256
-- `per_device_eval_batch_size`: 256
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
 - `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`: 2e-05
-- `weight_decay`: 0.001
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
 - `adam_beta1`: 0.9
 - `adam_beta2`: 0.999
 - `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1.0
-- `num_train_epochs`: 3.0
-- `max_steps`: 3510
+- `max_grad_norm`: 1
+- `num_train_epochs`: 3
+- `max_steps`: -1
 - `lr_scheduler_type`: linear
 - `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.1
+- `warmup_ratio`: 0.0
 - `warmup_steps`: 0
 - `log_level`: passive
 - `log_level_replica`: warning
@@ -363,14 +228,14 @@ You can finetune this model on your own dataset.
 - `tpu_num_cores`: None
 - `tpu_metrics_debug`: False
 - `debug`: []
-- `dataloader_drop_last`: True
-- `dataloader_num_workers`: 1
-- `dataloader_prefetch_factor`: 1
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
 - `past_index`: -1
 - `disable_tqdm`: False
 - `remove_unused_columns`: True
 - `label_names`: None
-- `load_best_model_at_end`: True
+- `load_best_model_at_end`: False
 - `ignore_data_skip`: False
 - `fsdp`: []
 - `fsdp_min_num_params`: 0
@@ -380,23 +245,23 @@ You can finetune this model on your own dataset.
 - `parallelism_config`: None
 - `deepspeed`: None
 - `label_smoothing_factor`: 0.0
-- `optim`: adamw_torch
+- `optim`: adamw_torch_fused
 - `optim_args`: None
 - `adafactor`: False
 - `group_by_length`: False
 - `length_column_name`: length
 - `project`: huggingface
 - `trackio_space_id`: trackio
-- `ddp_find_unused_parameters`: False
+- `ddp_find_unused_parameters`: None
 - `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`: True
+- `push_to_hub`: False
 - `resume_from_checkpoint`: None
-- `hub_model_id`: redis/model-b-structured
+- `hub_model_id`: None
 - `hub_strategy`: every_save
 - `hub_private_repo`: None
 - `hub_always_push`: False
@@ -423,58 +288,31 @@ You can finetune this model on your own dataset.
 - `neftune_noise_alpha`: None
 - `optim_target_modules`: None
 - `batch_eval_metrics`: False
-- `eval_on_start`: True
+- `eval_on_start`: False
 - `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`: proportional
+- `multi_dataset_batch_sampler`: round_robin
 - `router_mapping`: {}
 - `learning_rate_mapping`: {}
 
 </details>
 
 ### Training Logs
-| Epoch  | Step | Training Loss | Validation Loss | val_cosine_ndcg@10 |
-|:------:|:----:|:-------------:|:---------------:|:------------------:|
-| 0      | 0    | -             | 1.7213          | 0.8443             |
-| 0.2849 | 100  | 1.4895        | 0.5433          | 0.9000             |
-| 0.5698 | 200  | 0.3823        | 0.1877          | 0.9346             |
-| 0.8547 | 300  | 0.2463        | 0.1401          | 0.9405             |
-| 1.1396 | 400  | 0.1987        | 0.1233          | 0.9437             |
-| 1.4245 | 500  | 0.1724        | 0.1083          | 0.9465             |
-| 1.7094 | 600  | 0.1578        | 0.0996          | 0.9481             |
-| 1.9943 | 700  | 0.1425        | 0.0940          | 0.9494             |
-| 2.2792 | 800  | 0.1319        | 0.0896          | 0.9498             |
-| 2.5641 | 900  | 0.1221        | 0.0863          | 0.9505             |
-| 2.8490 | 1000 | 0.1124        | 0.0833          | 0.9512             |
-| 3.1339 | 1100 | 0.1116        | 0.0816          | 0.9514             |
-| 3.4188 | 1200 | 0.1019        | 0.0808          | 0.9522             |
-| 3.7037 | 1300 | 0.1007        | 0.0784          | 0.9522             |
-| 3.9886 | 1400 | 0.1009        | 0.0766          | 0.9525             |
-| 4.2735 | 1500 | 0.0889        | 0.0759          | 0.9533             |
-| 4.5584 | 1600 | 0.0891        | 0.0748          | 0.9536             |
-| 4.8433 | 1700 | 0.0874        | 0.0734          | 0.9534             |
-| 5.1282 | 1800 | 0.0856        | 0.0721          | 0.9539             |
-| 5.4131 | 1900 | 0.082         | 0.0715          | 0.9544             |
-| 5.6980 | 2000 | 0.0821        | 0.0704          | 0.9540             |
-| 5.9829 | 2100 | 0.0804        | 0.0699          | 0.9540             |
-| 6.2678 | 2200 | 0.076         | 0.0694          | 0.9544             |
-| 6.5527 | 2300 | 0.0729        | 0.0693          | 0.9546             |
-| 6.8376 | 2400 | 0.0758        | 0.0688          | 0.9547             |
-| 7.1225 | 2500 | 0.0728        | 0.0673          | 0.9547             |
-| 7.4074 | 2600 | 0.0707        | 0.0678          | 0.9546             |
-| 7.6923 | 2700 | 0.0695        | 0.0678          | 0.9549             |
-| 7.9772 | 2800 | 0.0716        | 0.0671          | 0.9546             |
-| 8.2621 | 2900 | 0.0709        | 0.0668          | 0.9544             |
-| 8.5470 | 3000 | 0.0692        | 0.0671          | 0.9551             |
-| 8.8319 | 3100 | 0.0694        | 0.0666          | 0.9547             |
-| 9.1168 | 3200 | 0.0713        | 0.0667          | 0.9547             |
-| 9.4017 | 3300 | 0.0682        | 0.0665          | 0.9546             |
-| 9.6866 | 3400 | 0.07          | 0.0664          | 0.9547             |
-| 9.9715 | 3500 | 0.07          | 0.0664          | 0.9546             |
+| 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        |
 
 
 ### Framework Versions

eval/Information-Retrieval_evaluation_val_results.csv

@@ -38,3 +38,144 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Precisi
 9.401709401709402,3300,0.9106,0.969,0.984,0.9106,0.9106,0.323,0.969,0.19679999999999995,0.984,0.9106,0.9403533333333333,0.9417519841269842,0.9546344737257203,0.9422058505967718
 9.686609686609687,3400,0.9106,0.969,0.9838,0.9106,0.9106,0.323,0.969,0.19675999999999996,0.9838,0.9106,0.94029,0.9417384126984129,0.9546651626751027,0.9421697455120135
 9.971509971509972,3500,0.9104,0.9688,0.9842,0.9104,0.9104,0.32293333333333335,0.9688,0.19683999999999996,0.9842,0.9104,0.9402533333333333,0.9416303174603176,0.954585167414727,0.9420641228013908
+0,0,0.708475,0.796825,0.822125,0.708475,0.708475,0.26560833333333334,0.796825,0.164425,0.822125,0.708475,0.7545720833333283,0.7589311706349164,0.7821232471271422,0.762190078448873
+0.07112375533428165,100,0.722325,0.835475,0.864475,0.722325,0.722325,0.27849166666666664,0.835475,0.17289500000000002,0.864475,0.722325,0.7807887499999926,0.7853636309523765,0.8129700440800849,0.788695746119173
+0.1422475106685633,200,0.775525,0.865825,0.893425,0.775525,0.775525,0.2886083333333333,0.865825,0.178685,0.893425,0.775525,0.8226208333333254,0.8270209226190415,0.8510344343464452,0.8297694974089365
+0.21337126600284495,300,0.7953,0.8738,0.898525,0.7953,0.7953,0.2912666666666666,0.8738,0.17970500000000003,0.898525,0.7953,0.8362499999999936,0.8402300595238037,0.8614711546093078,0.8429135761695575
+0.2844950213371266,400,0.80075,0.875975,0.8999,0.80075,0.80075,0.29199166666666665,0.875975,0.17998000000000003,0.8999,0.80075,0.8400066666666598,0.844234613095233,0.8652126035164182,0.8469108693511953
+0.35561877667140823,500,0.804275,0.87805,0.90305,0.804275,0.804275,0.29268333333333324,0.87805,0.18061000000000002,0.90305,0.804275,0.8431766666666619,0.8472814087301547,0.8680716987185689,0.849959335480741
+0.4267425320056899,600,0.807025,0.88005,0.9059,0.807025,0.807025,0.29334999999999994,0.88005,0.18118,0.9059,0.807025,0.8457383333333277,0.8498279365079326,0.8706979594584189,0.8524284487021079
+0.49786628733997157,700,0.80925,0.88155,0.9079,0.80925,0.80925,0.29384999999999994,0.88155,0.18158000000000002,0.9079,0.80925,0.8476441666666613,0.8517574107142803,0.8726681323345749,0.8543576926337788
+0.5689900426742532,800,0.8109,0.883625,0.9099,0.8109,0.8109,0.2945416666666666,0.883625,0.18198000000000003,0.9099,0.8109,0.8495879166666622,0.8536860714285669,0.8745685072106874,0.8563012372130501
+0.6401137980085349,900,0.81195,0.885025,0.910475,0.81195,0.81195,0.29500833333333326,0.885025,0.18209500000000003,0.910475,0.81195,0.850385416666661,0.8545485218253918,0.8754790911316688,0.8571486995111011
+0.7112375533428165,1000,0.8134,0.886325,0.911625,0.8134,0.8134,0.2954416666666666,0.886325,0.182325,0.911625,0.8134,0.851712916666662,0.8559180257936456,0.8769045253521254,0.858461313040403
+0.7823613086770982,1100,0.814875,0.886975,0.9132,0.814875,0.814875,0.2956583333333333,0.886975,0.18264,0.9132,0.814875,0.8530883333333288,0.8571790972222177,0.8780328288384593,0.8597917042367506
+0.8534850640113798,1200,0.815225,0.8876,0.914625,0.815225,0.815225,0.2958666666666666,0.8876,0.182925,0.914625,0.815225,0.8536179166666618,0.8577433928571372,0.8788102573542441,0.8603604705654514
+0.9246088193456614,1300,0.8168,0.88965,0.916,0.8168,0.8168,0.29655,0.88965,0.18320000000000003,0.916,0.8168,0.8553495833333291,0.8594912698412667,0.8805103132423321,0.8620812371800759
+0.9957325746799431,1400,0.81755,0.8904,0.91635,0.81755,0.81755,0.29679999999999995,0.8904,0.18327000000000002,0.91635,0.81755,0.8559383333333279,0.8601746230158671,0.8812821892817625,0.8627088755223402
+1.0668563300142249,1500,0.818025,0.890825,0.91855,0.818025,0.818025,0.29694166666666666,0.890825,0.18371000000000004,0.91855,0.818025,0.8568879166666625,0.8609777380952331,0.8821959740808727,0.8634983104603688
+1.1379800853485065,1600,0.818,0.8911,0.9188,0.818,0.818,0.29703333333333326,0.8911,0.18376,0.9188,0.818,0.8568829166666623,0.8609047619047576,0.8821272443292048,0.8634440919229286
+1.209103840682788,1700,0.819175,0.89195,0.9181,0.819175,0.819175,0.2973166666666667,0.89195,0.18362000000000003,0.9181,0.819175,0.8575737499999945,0.8617549107142799,0.882844384353523,0.8642749205132398
+1.2802275960170697,1800,0.8194,0.8924,0.919725,0.8194,0.8194,0.29746666666666666,0.8924,0.18394500000000003,0.919725,0.8194,0.8581379166666618,0.8622184722222166,0.8834552221414264,0.8647068630314819
+1.3513513513513513,1900,0.8191,0.8923,0.9191,0.8191,0.8191,0.2974333333333333,0.8923,0.18382000000000004,0.9191,0.8191,0.8577912499999948,0.8620219146825335,0.8833214194983335,0.8645483869343061
+1.422475106685633,2000,0.8196,0.892925,0.919325,0.8196,0.8196,0.2976416666666666,0.892925,0.183865,0.919325,0.8196,0.8582274999999945,0.8625463194444392,0.8839596520387931,0.8650054306218091
+1.4935988620199145,2100,0.819925,0.893125,0.9203,0.819925,0.819925,0.2977083333333333,0.893125,0.18406,0.9203,0.819925,0.8587529166666618,0.8630120535714239,0.88446025808528,0.8654665677460687
+1.5647226173541964,2200,0.820125,0.894225,0.920375,0.820125,0.820125,0.298075,0.894225,0.18407500000000002,0.920375,0.820125,0.8592241666666613,0.8636013293650736,0.8851266057760023,0.8659959512491583
+1.635846372688478,2300,0.820075,0.8932,0.9209,0.820075,0.820075,0.29773333333333324,0.8932,0.18418,0.9209,0.820075,0.8588995833333279,0.8632591369047553,0.8849475053016227,0.8656661752985969
+1.7069701280227596,2400,0.82185,0.8952,0.9231,0.82185,0.82185,0.29839999999999994,0.8952,0.18462000000000003,0.9231,0.82185,0.8608804166666614,0.8651157341269778,0.8866948372363526,0.8674770134243758
+1.7780938833570412,2500,0.82185,0.8954,0.923,0.82185,0.82185,0.2984666666666666,0.8954,0.18460000000000001,0.923,0.82185,0.8608999999999958,0.8652058432539638,0.8868494073657219,0.8675460895736036
+1.8492176386913228,2600,0.8215,0.895275,0.92175,0.8215,0.8215,0.298425,0.895275,0.18435000000000004,0.92175,0.8215,0.8603341666666618,0.8646135416666603,0.8860526209198091,0.8670679286069957
+1.9203413940256047,2700,0.820625,0.8945,0.922,0.820625,0.820625,0.2981666666666666,0.8945,0.18440000000000004,0.922,0.820625,0.8598466666666612,0.8641913293650736,0.8859156872252449,0.8665781551131037
+1.991465149359886,2800,0.822025,0.895975,0.922925,0.822025,0.822025,0.2986583333333333,0.895975,0.18458500000000003,0.922925,0.822025,0.8611374999999949,0.8654293253968208,0.8869782054645499,0.8678350492219843
+2.062588904694168,2900,0.822375,0.8974,0.9234,0.822375,0.822375,0.2991333333333333,0.8974,0.18468000000000004,0.9234,0.822375,0.8616691666666622,0.8659512003968208,0.8874814593992038,0.868350202612051
+2.1337126600284497,3000,0.82195,0.8957,0.92255,0.82195,0.82195,0.2985666666666666,0.8957,0.18451000000000004,0.92255,0.82195,0.8608883333333277,0.8652523214285655,0.8868455935332514,0.8676710098840409
+2.204836415362731,3100,0.822625,0.8968,0.924125,0.822625,0.822625,0.29893333333333333,0.8968,0.184825,0.924125,0.822625,0.8619379166666613,0.8662695436507878,0.8879605042451206,0.868627403228108
+2.275960170697013,3200,0.822275,0.896475,0.92305,0.822275,0.822275,0.29882499999999995,0.896475,0.18461000000000002,0.92305,0.822275,0.861370416666661,0.8657300198412631,0.8873368206534243,0.8681311777983121
+2.3470839260312943,3300,0.822575,0.8966,0.923925,0.822575,0.822575,0.29886666666666667,0.8966,0.18478500000000003,0.923925,0.822575,0.8617070833333279,0.8659898313492007,0.8876205256797713,0.868391252660771
+2.418207681365576,3400,0.823025,0.89705,0.924575,0.823025,0.823025,0.2990166666666666,0.89705,0.184915,0.924575,0.823025,0.8622604166666619,0.8665290773809465,0.8881827229716455,0.8689210814093723
+2.4893314366998576,3500,0.823625,0.897725,0.925475,0.823625,0.823625,0.29924166666666663,0.897725,0.18509500000000004,0.925475,0.823625,0.8629520833333283,0.8672388888888832,0.8889497027095765,0.8695685936615529
+2.5604551920341394,3600,0.8235,0.89685,0.924175,0.8235,0.8235,0.2989499999999999,0.89685,0.184835,0.924175,0.8235,0.8622354166666617,0.8666268154761853,0.888341415540098,0.8689953073597675
+2.6315789473684212,3700,0.82355,0.897425,0.92415,0.82355,0.82355,0.2991416666666666,0.897425,0.18483000000000005,0.92415,0.82355,0.8624670833333292,0.8669097222222183,0.8886308654439339,0.8692619959122819
+2.7027027027027026,3800,0.8238,0.897675,0.924725,0.8238,0.8238,0.29922499999999996,0.897675,0.18494500000000005,0.924725,0.8238,0.8628454166666616,0.8672871428571367,0.8891080403797096,0.8695675304567493
+2.7738264580369845,3900,0.823575,0.897225,0.924975,0.823575,0.823575,0.2990749999999999,0.897225,0.18499500000000002,0.924975,0.823575,0.8627349999999951,0.8671197817460253,0.8889427948375415,0.8694255979358242
+2.844950213371266,4000,0.8246,0.898625,0.9255,0.8246,0.8246,0.2995416666666666,0.898625,0.1851,0.9255,0.8246,0.8636449999999944,0.8680026785714217,0.8896766870518819,0.8703092429631565
+2.9160739687055477,4100,0.82475,0.8984,0.925175,0.82475,0.82475,0.29946666666666666,0.8984,0.18503500000000003,0.925175,0.82475,0.863664166666661,0.8680787599206262,0.8897809878315085,0.8703838923006196
+2.987197724039829,4200,0.824475,0.898775,0.925925,0.824475,0.824475,0.2995916666666666,0.898775,0.18518500000000002,0.925925,0.824475,0.8637179166666615,0.8681762301587244,0.8900659161989499,0.8704322254975827
+3.058321479374111,4300,0.823775,0.898125,0.925725,0.823775,0.823775,0.2993749999999999,0.898125,0.18514500000000003,0.925725,0.823775,0.8631258333333284,0.8674574305555496,0.889263149093025,0.8697791518408887
+3.1294452347083928,4400,0.82425,0.8994,0.92655,0.82425,0.82425,0.2997999999999999,0.8994,0.18531,0.92655,0.82425,0.8638191666666611,0.8681279563491986,0.8899985654190258,0.8704186236514423
+3.200568990042674,4500,0.824375,0.897525,0.925025,0.824375,0.824375,0.299175,0.897525,0.18500500000000003,0.925025,0.824375,0.8632137499999953,0.867598730158724,0.8893111625023677,0.869929135071786
+3.271692745376956,4600,0.824775,0.8987,0.926425,0.824775,0.824775,0.2995666666666666,0.8987,0.185285,0.926425,0.824775,0.863976666666661,0.868326567460311,0.8901824492164305,0.8705952707897266
+3.3428165007112374,4700,0.82425,0.89855,0.92595,0.82425,0.82425,0.29951666666666665,0.89855,0.18519000000000002,0.92595,0.82425,0.8635716666666605,0.8679974404761835,0.889916074777261,0.870291257332588
+3.413940256045519,4800,0.824425,0.8992,0.926925,0.824425,0.824425,0.2997333333333333,0.8992,0.18538500000000005,0.926925,0.824425,0.8640862499999946,0.8683873015872947,0.8902968657758403,0.8706472282678136
+3.485064011379801,4900,0.8255,0.900225,0.9274,0.8255,0.8255,0.300075,0.900225,0.18548,0.9274,0.8255,0.864929583333328,0.8693004464285641,0.8911555528211864,0.8715296867059648
+3.5561877667140824,5000,0.826175,0.9004,0.92745,0.826175,0.826175,0.30013333333333325,0.9004,0.18549000000000002,0.92745,0.826175,0.8653699999999938,0.8697670436507864,0.8915337858893878,0.8719912796724624
+3.6273115220483643,5100,0.825575,0.89855,0.926725,0.825575,0.825575,0.2995166666666666,0.89855,0.18534500000000004,0.926725,0.825575,0.864612083333328,0.8690407142857088,0.8908701769082038,0.8712962428055211
+3.6984352773826457,5200,0.8252,0.899325,0.92635,0.8252,0.8252,0.299775,0.899325,0.18527000000000002,0.92635,0.8252,0.8644154166666612,0.8688983134920578,0.89076275197231,0.8711567837909996
+3.7695590327169275,5300,0.82605,0.900025,0.927225,0.82605,0.82605,0.3000083333333333,0.900025,0.18544500000000005,0.927225,0.82605,0.8653387499999955,0.8697499603174547,0.8915223711969326,0.871992943283452
+3.8406827880512093,5400,0.826275,0.9004,0.92755,0.826275,0.826275,0.3001333333333333,0.9004,0.18551000000000004,0.92755,0.826275,0.865512916666662,0.8699264781745971,0.8917181202681815,0.8721455201676681
+3.9118065433854907,5500,0.825775,0.899925,0.927325,0.825775,0.825775,0.29997499999999994,0.899925,0.18546500000000002,0.927325,0.825775,0.8650508333333288,0.8694735218253918,0.8913486775145258,0.8716941997380186
+3.9829302987197726,5600,0.826125,0.89995,0.927225,0.826125,0.826125,0.2999833333333333,0.89995,0.18544500000000003,0.927225,0.826125,0.865341249999995,0.8697941666666599,0.89162530417659,0.8720109405246621
+4.054054054054054,5700,0.826475,0.90025,0.9278,0.826475,0.826475,0.3000833333333333,0.90025,0.18556,0.9278,0.826475,0.8656508333333292,0.8699955456349148,0.8917350022910326,0.8722532738352884
+4.125177809388336,5800,0.826225,0.899475,0.927,0.826225,0.826225,0.299825,0.899475,0.1854,0.927,0.826225,0.8651508333333295,0.869614156746027,0.89143847123497,0.8718547533641309
+4.196301564722617,5900,0.825725,0.89975,0.9276,0.825725,0.825725,0.2999166666666666,0.89975,0.18552000000000002,0.9276,0.825725,0.8650741666666627,0.8695133035714238,0.8914381678978238,0.8717376488223952
+4.2674253200568995,6000,0.825175,0.89985,0.927075,0.825175,0.825175,0.2999499999999999,0.89985,0.18541500000000002,0.927075,0.825175,0.8646229166666631,0.8690451587301552,0.8909441732114654,0.8713219430870955
+4.338549075391181,6100,0.826325,0.9,0.928075,0.826325,0.826325,0.29999999999999993,0.9,0.18561500000000006,0.928075,0.826325,0.8655245833333285,0.8699142559523754,0.8918191631811841,0.8721377155219839
+4.409672830725462,6200,0.825775,0.9,0.9274,0.825775,0.825775,0.3,0.9,0.18548,0.9274,0.825775,0.865164999999996,0.8696455753968206,0.8915460140151276,0.8718917068937276
+4.480796586059744,6300,0.82585,0.900625,0.928175,0.82585,0.82585,0.3002083333333333,0.900625,0.18563500000000002,0.928175,0.82585,0.8654541666666612,0.8698656249999948,0.8918297577207097,0.8720836684391886
+4.551920341394026,6400,0.8262,0.901,0.9284,0.8262,0.8262,0.3003333333333333,0.901,0.18568000000000004,0.9284,0.8262,0.8656649999999949,0.870096775793644,0.8921152393297598,0.8722803359988235
+4.623044096728307,6500,0.825825,0.900225,0.92815,0.825825,0.825825,0.300075,0.900225,0.18563000000000002,0.92815,0.825825,0.8654254166666627,0.8698505357142811,0.8918221979339549,0.8720642323644188
+4.694167852062589,6600,0.826825,0.9006,0.928325,0.826825,0.826825,0.30019999999999997,0.9006,0.18566500000000002,0.928325,0.826825,0.8660116666666614,0.8704902480158675,0.8924644328014245,0.8726653432105024
+4.76529160739687,6700,0.82635,0.900575,0.928125,0.82635,0.82635,0.3001916666666666,0.900575,0.185625,0.928125,0.82635,0.8656424999999954,0.8701348214285656,0.8921800055683209,0.8723029664572237
+4.836415362731152,6800,0.825825,0.90015,0.927925,0.825825,0.825825,0.30004999999999993,0.90015,0.18558500000000003,0.927925,0.825825,0.8652804166666618,0.8697154662698348,0.8916889085785515,0.8719573734033452
+4.907539118065434,6900,0.825875,0.9007,0.9283,0.825875,0.825875,0.3002333333333333,0.9007,0.18566000000000005,0.9283,0.825875,0.8655537499999949,0.8700121825396756,0.8920441415559691,0.8722158863715503
+4.978662873399715,7000,0.82585,0.90085,0.92835,0.82585,0.82585,0.30028333333333324,0.90085,0.18567000000000003,0.92835,0.82585,0.8656495833333278,0.8700978373015802,0.8921329634415721,0.8722989989753108
+5.049786628733997,7100,0.82615,0.900125,0.928025,0.82615,0.82615,0.3000416666666666,0.900125,0.18560500000000002,0.928025,0.82615,0.8655370833333286,0.8699936607142797,0.8919609460457947,0.8722228385351324
+5.120910384068279,7200,0.8266,0.90045,0.928625,0.8266,0.8266,0.30015,0.90045,0.18572500000000003,0.928625,0.8266,0.8659883333333286,0.8704057440476135,0.8923903227770039,0.8726006052318324
+5.19203413940256,7300,0.826275,0.900525,0.928325,0.826275,0.826275,0.30017499999999997,0.900525,0.18566500000000005,0.928325,0.826275,0.865821666666663,0.8703211111111067,0.8923685679772724,0.8725058346472185
+5.2631578947368425,7400,0.82715,0.900825,0.9287,0.82715,0.82715,0.30027499999999996,0.900825,0.18574000000000004,0.9287,0.82715,0.866372499999995,0.8708181845238036,0.8927230306601641,0.873016518689878
+5.334281650071124,7500,0.8269,0.9007,0.92865,0.8269,0.8269,0.3002333333333333,0.9007,0.18573,0.92865,0.8269,0.8661345833333295,0.870578591269836,0.8925413608695965,0.8727783195270331
+5.405405405405405,7600,0.8273,0.902125,0.92945,0.8273,0.8273,0.3007083333333333,0.902125,0.18589000000000003,0.92945,0.8273,0.866819999999996,0.8712253273809459,0.8931676895510297,0.8734009300232072
+5.476529160739687,7700,0.8261,0.90025,0.9283,0.8261,0.8261,0.3000833333333333,0.90025,0.18566000000000005,0.9283,0.8261,0.865633333333329,0.8700428869047561,0.8919629226946234,0.8722891306212861
+5.547652916073969,7800,0.826975,0.9014,0.929225,0.826975,0.826975,0.30046666666666666,0.9014,0.18584499999999998,0.929225,0.826975,0.8665362499999958,0.8709029761904703,0.8928165883433447,0.8731121920648562
+5.61877667140825,7900,0.827225,0.9016,0.92965,0.827225,0.827225,0.3005333333333333,0.9016,0.18593,0.92965,0.827225,0.8667216666666622,0.8710348511904691,0.8929259824926039,0.8732431056526635
+5.689900426742532,8000,0.827325,0.901575,0.9293,0.827325,0.827325,0.30052499999999993,0.901575,0.18586000000000003,0.9293,0.827325,0.8667749999999959,0.8711936111111052,0.8931338078257104,0.8733803433982779
+5.761024182076814,8100,0.827325,0.901325,0.9294,0.827325,0.827325,0.30044166666666666,0.901325,0.18588000000000005,0.9294,0.827325,0.8668224999999962,0.8711923611111059,0.8930914853077098,0.8733914561201167
+5.832147937411095,8200,0.8271,0.90135,0.929575,0.8271,0.8271,0.30045,0.90135,0.18591500000000002,0.929575,0.8271,0.8665966666666624,0.870971617063485,0.8929716232701749,0.8731473958399926
+5.903271692745377,8300,0.827375,0.901275,0.929575,0.827375,0.827375,0.30042499999999994,0.901275,0.18591500000000005,0.929575,0.827375,0.8667629166666622,0.8711837003968187,0.8931756127653813,0.8733501042216866
+5.974395448079658,8400,0.827075,0.9014,0.929225,0.827075,0.827075,0.3004666666666666,0.9014,0.18584500000000004,0.929225,0.827075,0.8664962499999962,0.8709454166666614,0.8929605309745368,0.8731388106861432
+6.0455192034139404,8500,0.826925,0.901175,0.929275,0.826925,0.826925,0.30039166666666667,0.901175,0.18585500000000002,0.929275,0.826925,0.8663720833333296,0.8708128869047569,0.8928670137625475,0.8729984483900762
+6.116642958748222,8600,0.827375,0.901275,0.929575,0.827375,0.827375,0.3004249999999999,0.901275,0.18591500000000005,0.929575,0.827375,0.8666758333333299,0.8710950198412644,0.8931014973905476,0.8732727102367857
+6.187766714082503,8700,0.827325,0.901275,0.929825,0.827325,0.827325,0.300425,0.901275,0.18596500000000002,0.929825,0.827325,0.8667737499999961,0.8711758432539642,0.8932032489373747,0.8733509090687692
+6.2588904694167855,8800,0.8273,0.901075,0.929525,0.8273,0.8273,0.3003583333333333,0.901075,0.18590500000000001,0.929525,0.8273,0.8666895833333296,0.8711485615079311,0.8931887967740513,0.8733185112174393
+6.330014224751067,8900,0.8273,0.902075,0.929975,0.8273,0.8273,0.3006916666666666,0.902075,0.185995,0.929975,0.8273,0.866963333333329,0.8713748511904703,0.8934143433391218,0.8735371248005948
+6.401137980085348,9000,0.82755,0.9019,0.929925,0.82755,0.82755,0.3006333333333333,0.9019,0.18598500000000004,0.929925,0.82755,0.8669908333333295,0.8713906249999939,0.8933946978262389,0.8735523391943721
+6.472261735419631,9100,0.827075,0.90195,0.9295,0.827075,0.827075,0.30065,0.90195,0.1859,0.9295,0.827075,0.8666599999999954,0.8711001884920575,0.8931327166161569,0.873279831908762
+6.543385490753912,9200,0.827275,0.901325,0.929325,0.827275,0.827275,0.3004416666666666,0.901325,0.185865,0.929325,0.827275,0.866724999999996,0.8711729761904704,0.893137421919378,0.8733594546877983
+6.614509246088193,9300,0.82805,0.9021,0.93015,0.82805,0.82805,0.30069999999999997,0.9021,0.18603000000000003,0.93015,0.82805,0.8674033333333282,0.8718179761904695,0.8937801242746259,0.8739678233587836
+6.685633001422475,9400,0.8274,0.90145,0.92975,0.8274,0.8274,0.3004833333333333,0.90145,0.18595000000000003,0.92975,0.8274,0.8669149999999956,0.8713803968253898,0.8934607392567395,0.8735101273721
+6.756756756756757,9500,0.827425,0.90195,0.93005,0.827425,0.827425,0.30065,0.90195,0.18601000000000004,0.93005,0.827425,0.8670141666666629,0.8714513888888825,0.8935497802488053,0.8735801816011066
+6.827880512091038,9600,0.827225,0.90175,0.9299,0.827225,0.827225,0.3005833333333333,0.90175,0.18598,0.9299,0.827225,0.8668416666666624,0.8713365873015803,0.8935022790918725,0.8734559739901633
+6.89900426742532,9700,0.827375,0.90225,0.9302,0.827375,0.827375,0.30074999999999996,0.90225,0.18604000000000004,0.9302,0.827375,0.8670516666666624,0.8714862400793584,0.8935805344348627,0.8736238052627762
+6.970128022759602,9800,0.82675,0.90165,0.930025,0.82675,0.82675,0.30055,0.90165,0.18600500000000003,0.930025,0.82675,0.8666212499999965,0.8710792857142798,0.893291732812247,0.8732089879292773
+7.0412517780938835,9900,0.827025,0.90265,0.9307,0.827025,0.827025,0.3008833333333333,0.90265,0.18614,0.9307,0.827025,0.8670595833333293,0.871396656746025,0.893538239528841,0.8735295595199406
+7.112375533428165,10000,0.827425,0.902575,0.930475,0.827425,0.827425,0.3008583333333333,0.902575,0.18609500000000004,0.930475,0.827425,0.8672554166666627,0.8716640079365022,0.8937487432845319,0.873803083516281
+7.183499288762446,10100,0.8271,0.90185,0.93035,0.8271,0.8271,0.3006166666666666,0.90185,0.18607000000000004,0.93035,0.8271,0.866940833333329,0.8713414087301533,0.893460804923355,0.8734842714542749
+7.2546230440967285,10200,0.8273,0.9024,0.930825,0.8273,0.8273,0.3008,0.9024,0.18616500000000002,0.930825,0.8273,0.8672354166666637,0.871576527777773,0.8936706222925621,0.8737201708523414
+7.32574679943101,10300,0.827,0.90195,0.93035,0.827,0.827,0.30065,0.90195,0.18607000000000004,0.93035,0.827,0.8668745833333292,0.8712707142857095,0.8933687994725479,0.8734377113367171
+7.396870554765291,10400,0.827425,0.90205,0.93045,0.827425,0.827425,0.3006833333333333,0.90205,0.18609000000000006,0.93045,0.827425,0.8671599999999958,0.8715786904761852,0.8936834412141746,0.8737168495708162
+7.467994310099574,10500,0.827675,0.902275,0.930375,0.827675,0.827675,0.3007583333333333,0.902275,0.18607500000000002,0.930375,0.827675,0.8671958333333292,0.8715664880952326,0.8935959272616397,0.8737339779395145
+7.539118065433855,10600,0.827525,0.902075,0.930825,0.827525,0.827525,0.3006916666666666,0.902075,0.186165,0.930825,0.827525,0.8673195833333289,0.8716814186507876,0.8937695384748613,0.8738217383831259
+7.610241820768136,10700,0.826825,0.901825,0.930125,0.826825,0.826825,0.3006083333333333,0.901825,0.186025,0.930125,0.826825,0.8666412499999949,0.8710677281745962,0.8932242969670978,0.8732328510015733
+7.681365576102419,10800,0.827125,0.9014,0.9303,0.827125,0.827125,0.3004666666666666,0.9014,0.18606000000000003,0.9303,0.827125,0.8668729166666626,0.8712897321428514,0.8934112707000093,0.8734470132059551
+7.7524893314367,10900,0.827725,0.90215,0.930775,0.827725,0.827725,0.30071666666666663,0.90215,0.18615500000000001,0.930775,0.827725,0.8674787499999947,0.8718391567460251,0.8938481523794622,0.8740012494161281
+7.823613086770981,11000,0.827725,0.902775,0.931,0.827725,0.827725,0.3009249999999999,0.902775,0.18620000000000003,0.931,0.827725,0.8675758333333291,0.871905257936502,0.8939239746475901,0.8740667873515862
+7.894736842105263,11100,0.8274,0.9023,0.9305,0.8274,0.8274,0.3007666666666666,0.9023,0.18610000000000002,0.9305,0.8274,0.8671412499999951,0.8715991567460251,0.8937906785238047,0.8737041181091459
+7.965860597439545,11200,0.82765,0.902725,0.93115,0.82765,0.82765,0.3009083333333333,0.902725,0.18623,0.93115,0.82765,0.867559166666662,0.8719196825396771,0.8940223198634536,0.874045803227272
+8.036984352773827,11300,0.827575,0.9023,0.9309,0.827575,0.827575,0.3007666666666666,0.9023,0.18618,0.9309,0.827575,0.8673533333333288,0.8717183928571373,0.8937985230658438,0.8738695292087821
+8.108108108108109,11400,0.8272,0.902325,0.9307,0.8272,0.8272,0.3007749999999999,0.902325,0.18614,0.9307,0.8272,0.8671591666666624,0.8715653869047552,0.8937392409414204,0.8736849934539683
+8.17923186344239,11500,0.827425,0.902375,0.93065,0.827425,0.827425,0.3007916666666666,0.902375,0.18613000000000002,0.93065,0.827425,0.8672470833333283,0.8716846726190411,0.8938569816776198,0.8737965294795397
+8.250355618776672,11600,0.826975,0.902,0.93055,0.826975,0.826975,0.3006666666666666,0.902,0.18611000000000003,0.93055,0.826975,0.8669816666666622,0.8713896130952322,0.8935380269606285,0.8735380533496878
+8.321479374110954,11700,0.82735,0.902175,0.93045,0.82735,0.82735,0.30072499999999996,0.902175,0.18609000000000006,0.93045,0.82735,0.8672008333333284,0.8716245535714215,0.8937331752233314,0.8737676385625377
+8.392603129445234,11800,0.827325,0.902075,0.930825,0.827325,0.827325,0.3006916666666666,0.902075,0.18616500000000002,0.930825,0.827325,0.8672229166666626,0.8716189682539626,0.8937900710030335,0.8737401825146364
+8.463726884779517,11900,0.82825,0.902375,0.93095,0.82825,0.82825,0.3007916666666666,0.902375,0.18619000000000002,0.93095,0.82825,0.867789999999996,0.8722008035714228,0.8942588368608063,0.8743165391515858
+8.534850640113799,12000,0.828025,0.902375,0.93065,0.828025,0.828025,0.3007916666666666,0.902375,0.18613000000000002,0.93065,0.828025,0.8675908333333291,0.8720352281745967,0.8941107124359962,0.8741616170195275
+8.60597439544808,12100,0.8274,0.90205,0.93065,0.8274,0.8274,0.3006833333333333,0.90205,0.18613000000000005,0.93065,0.8274,0.8671479166666625,0.8715756051587236,0.8937531540939795,0.8737020154783539
+8.677098150782362,12200,0.8279,0.902575,0.9311,0.8279,0.8279,0.3008583333333333,0.902575,0.18622000000000002,0.9311,0.8279,0.8676112499999951,0.8719609920634847,0.8940504954192067,0.8740880305749279
+8.748221906116642,12300,0.828025,0.9026,0.93095,0.828025,0.828025,0.3008666666666666,0.9026,0.18619000000000002,0.93095,0.828025,0.8676454166666625,0.872042718253962,0.8941141499515919,0.8741736270226745
+8.819345661450924,12400,0.827675,0.902525,0.930725,0.827675,0.827675,0.3008416666666666,0.902525,0.18614500000000003,0.930725,0.827675,0.8674170833333291,0.8718653571428513,0.8940080486660721,0.8739854046109397
+8.890469416785207,12500,0.828175,0.90275,0.930875,0.828175,0.828175,0.3009166666666666,0.90275,0.18617500000000003,0.930875,0.828175,0.8677533333333289,0.872185367063486,0.8942613640979699,0.874300699597779
+8.961593172119487,12600,0.828025,0.902575,0.930725,0.828025,0.828025,0.3008583333333333,0.902575,0.18614500000000003,0.930725,0.828025,0.8675987499999955,0.8720321726190418,0.8941092062004434,0.874160024782015
+9.03271692745377,12700,0.8279,0.9022,0.930825,0.8279,0.8279,0.30073333333333324,0.9022,0.18616500000000005,0.930825,0.8279,0.8675487499999955,0.8719490773809458,0.8940326075238234,0.874081080000685
+9.103840682788052,12800,0.8276,0.9022,0.930775,0.8276,0.8276,0.3007333333333333,0.9022,0.18615500000000001,0.930775,0.8276,0.867366249999996,0.8717785813492008,0.8939194902077963,0.8739011931174752
+9.174964438122332,12900,0.82805,0.902425,0.931075,0.82805,0.82805,0.3008083333333333,0.902425,0.18621500000000005,0.931075,0.82805,0.8677170833333292,0.8720985119047558,0.8941916115423987,0.8742162023078077
+9.246088193456615,13000,0.82815,0.902575,0.93085,0.82815,0.82815,0.3008583333333333,0.902575,0.18617000000000003,0.93085,0.82815,0.8676999999999966,0.8721441765872964,0.894261787826358,0.8742444867490919
+9.317211948790897,13100,0.828125,0.902475,0.930975,0.828125,0.828125,0.30082499999999995,0.902475,0.18619500000000003,0.930975,0.828125,0.8677062499999961,0.8721303075396769,0.8942478025806649,0.8742292661264696
+9.388335704125177,13200,0.8281,0.90265,0.931025,0.8281,0.8281,0.3008833333333333,0.90265,0.18620500000000004,0.931025,0.8281,0.8678104166666628,0.8722282936507881,0.8943284212825287,0.8743275076393491
+9.45945945945946,13300,0.828,0.902525,0.930925,0.828,0.828,0.3008416666666667,0.902525,0.18618500000000004,0.930925,0.828,0.8676795833333294,0.8720963095238036,0.8942075462606434,0.8742051203797395
+9.530583214793742,13400,0.828025,0.90255,0.9309,0.828025,0.828025,0.3008499999999999,0.90255,0.18618000000000004,0.9309,0.828025,0.8676949999999959,0.8721207837301521,0.89423621668204,0.8742209824708375
+9.601706970128022,13500,0.828075,0.902525,0.9309,0.828075,0.828075,0.3008416666666666,0.902525,0.18618,0.9309,0.828075,0.8677216666666631,0.8721371230158671,0.894225847427998,0.8742471205251703
+9.672830725462305,13600,0.82815,0.902675,0.93085,0.82815,0.82815,0.30089166666666667,0.902675,0.18617000000000003,0.93085,0.82815,0.8677495833333296,0.8721985813492004,0.8943215425397492,0.8742927261961481
+9.743954480796585,13700,0.828125,0.902625,0.93105,0.828125,0.828125,0.3008749999999999,0.902625,0.18621000000000001,0.93105,0.828125,0.8677633333333299,0.8721709722222164,0.8942849736621314,0.8742720567226947
+9.815078236130867,13800,0.827975,0.90265,0.931,0.827975,0.827975,0.3008833333333333,0.90265,0.18620000000000003,0.931,0.827975,0.8676762499999963,0.8720721230158671,0.894178246968981,0.8741864677767469
+9.88620199146515,13900,0.828025,0.9026,0.9311,0.828025,0.828025,0.3008666666666666,0.9026,0.18622000000000002,0.9311,0.828025,0.867727916666663,0.8721113690476133,0.8942189714318465,0.8742215047241502
+9.95732574679943,14000,0.828025,0.9027,0.931025,0.828025,0.828025,0.3008999999999999,0.9027,0.186205,0.931025,0.828025,0.8677179166666629,0.8721162896825339,0.8942284691055087,0.8742240723304836

final_metrics.json

@@ -1,16 +1,16 @@
 {
-  "val_cosine_accuracy@1": 0.908,
-  "val_cosine_accuracy@3": 0.9684,
-  "val_cosine_accuracy@5": 0.9834,
-  "val_cosine_precision@1": 0.908,
-  "val_cosine_precision@3": 0.3228,
-  "val_cosine_precision@5": 0.19667999999999997,
-  "val_cosine_recall@1": 0.908,
-  "val_cosine_recall@3": 0.9684,
-  "val_cosine_recall@5": 0.9834,
-  "val_cosine_ndcg@10": 0.9532296698470627,
-  "val_cosine_mrr@1": 0.908,
-  "val_cosine_mrr@5": 0.9386633333333337,
-  "val_cosine_mrr@10": 0.9400269841269848,
-  "val_cosine_map@100": 0.9404621256346036
+  "val_cosine_accuracy@1": 0.9104,
+  "val_cosine_accuracy@3": 0.9688,
+  "val_cosine_accuracy@5": 0.9842,
+  "val_cosine_precision@1": 0.9104,
+  "val_cosine_precision@3": 0.32293333333333335,
+  "val_cosine_precision@5": 0.19683999999999996,
+  "val_cosine_recall@1": 0.9104,
+  "val_cosine_recall@3": 0.9688,
+  "val_cosine_recall@5": 0.9842,
+  "val_cosine_ndcg@10": 0.9545809774353143,
+  "val_cosine_mrr@1": 0.9104,
+  "val_cosine_mrr@5": 0.9402433333333332,
+  "val_cosine_mrr@10": 0.9416250793650793,
+  "val_cosine_map@100": 0.9420576026548708
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:45b4ce06b2a2d061461c9871a66396c951f2af0a1f2815786891af9f01b422cc
+oid sha256:1a4f8953c7f93243ebe09fe7c0daa9cffac072577f9e8000399b77733108e58b
 size 114011616

training_args.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b6f4887f5ef478963a7e033106fee2bacd4dadbcb37a8042cfd307bbace16506
+oid sha256:97dfabb4d1b0f4e7472f63fe7d96632afc7ca09080738d20201c498fcf74eb45
 size 6161