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,-1,0.9208,0.9698,0.9842,0.9208,0.9208,0.3232666666666667,0.9698,0.19684,0.9842,0.9208,0.9460899999999998,0.9476021428571432,0.9593212690041523,0.9479260307963899
+-1,-1,0.9184,0.97,0.9852,0.9184,0.9184,0.3233333333333333,0.97,0.19703999999999997,0.9852,0.9184,0.9451366666666663,0.9466038095238087,0.9586270476620361,0.946959374340519

README.md

@@ -5,109 +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 polish my English skills?
   sentences:
-  - How do you cope with depression?
-  - How do I cope with my depression to keep my girlfriend?
-  - What does science say about crop circles?
-- source_sentence: Which is the best college for MBA in Delhi?
+  - How can we polish English skills?
+  - Why should I move to Israel as a Jew?
+  - What are vitamins responsible for?
+- source_sentence: Can I use the Kozuka Gothic Pro font as a font-face on my web site?
   sentences:
-  - Will time travel be possible in future?
-  - 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?
+  - Can I use the Kozuka Gothic Pro font as a font-face on my web site?
+  - Why are Google, Facebook, YouTube and other social networking sites banned in
+    China?
+  - What font is used in Bloomberg Terminal?
+- source_sentence: Is Quora the best Q&A site?
   sentences:
-  - What are examples of homonyms with meanings and sentences?
-  - What is poison mailbox?
-  - I was born on 29 may 1994 in pakistan city lahore my name is Ali Fraz Virk what
-    is my horoscope in details plz?
-- source_sentence: What are the differences between eccentric and concentric contraction?
-    What are some examples?
+  - What was the best Quora question ever?
+  - Is Quora the best inquiry site?
+  - Where do I buy Oway hair products online?
+- source_sentence: How can I customize my walking speed on Google Maps?
   sentences:
-  - Why is it when I pass my crush he always looks down at his phone?
-  - How widely accepted are credit cards at small businesses and restaurants in Bahrain?
-  - What are the differences between a concentric and eccentric movement?
-- source_sentence: 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?
+  - How do I bring back Google maps icon in my home screen?
+  - How many pages are there in all the Harry Potter books combined?
+  - How can I customize my walking speed on Google Maps?
+- source_sentence: DId something exist before the Big Bang?
   sentences:
-  - Who played the character of 'Russ' in friends?
-  - How do you use Dominos India WalkIn coupon code?
-  - 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?
+  - How can I improve my memory problem?
+  - Where can I buy Fairy Tail Manga?
+  - Is there a scientific name for what existed before the Big Bang?
 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: 0.9184
-      name: Cosine Accuracy@1
-    - type: cosine_accuracy@3
-      value: 0.97
-      name: Cosine Accuracy@3
-    - type: cosine_accuracy@5
-      value: 0.9852
-      name: Cosine Accuracy@5
-    - type: cosine_precision@1
-      value: 0.9184
-      name: Cosine Precision@1
-    - type: cosine_precision@3
-      value: 0.3233333333333333
-      name: Cosine Precision@3
-    - type: cosine_precision@5
-      value: 0.19703999999999997
-      name: Cosine Precision@5
-    - type: cosine_recall@1
-      value: 0.9184
-      name: Cosine Recall@1
-    - type: cosine_recall@3
-      value: 0.97
-      name: Cosine Recall@3
-    - type: cosine_recall@5
-      value: 0.9852
-      name: Cosine Recall@5
-    - type: cosine_ndcg@10
-      value: 0.9585962869405669
-      name: Cosine Ndcg@10
-    - type: cosine_mrr@1
-      value: 0.9184
-      name: Cosine Mrr@1
-    - type: cosine_mrr@5
-      value: 0.9451033333333331
-      name: Cosine Mrr@5
-    - type: cosine_mrr@10
-      value: 0.9465657142857136
-      name: Cosine Mrr@10
-    - type: cosine_map@100
-      value: 0.9469212791024237
-      name: Cosine Map@100
 ---
 
 # SentenceTransformer based on prajjwal1/bert-small
@@ -156,12 +85,12 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("redis/model-a-baseline")
+model = SentenceTransformer("sentence_transformers_model_id")
 # Run inference
 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?",
-    "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?",
-    'How do you use Dominos India WalkIn coupon code?',
+    'DId something exist before the Big Bang?',
+    'Is there a scientific name for what existed before the Big Bang?',
+    'Where can I buy Fairy Tail Manga?',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -170,9 +99,9 @@ print(embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[1.0000, 1.0000, 0.3427],
-#         [1.0000, 1.0000, 0.3427],
-#         [0.3427, 0.3427, 1.0001]])
+# tensor([[ 1.0000,  0.7596, -0.0398],
+#         [ 0.7596,  1.0000, -0.0308],
+#         [-0.0398, -0.0308,  1.0000]])
 ```
 
 <!--
@@ -199,32 +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  | 0.9184     |
-| cosine_accuracy@3  | 0.97       |
-| cosine_accuracy@5  | 0.9852     |
-| cosine_precision@1 | 0.9184     |
-| cosine_precision@3 | 0.3233     |
-| cosine_precision@5 | 0.197      |
-| cosine_recall@1    | 0.9184     |
-| cosine_recall@3    | 0.97       |
-| cosine_recall@5    | 0.9852     |
-| **cosine_ndcg@10** | **0.9586** |
-| cosine_mrr@1       | 0.9184     |
-| cosine_mrr@5       | 0.9451     |
-| cosine_mrr@10      | 0.9466     |
-| cosine_map@100     | 0.9469     |
-
 <!--
 ## Bias, Risks and Limitations
 
@@ -243,45 +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: 16.5 tokens</li><li>max: 67 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 do you hack an Instagram account?</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                                                                          |
-  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
-  | 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: 16.77 tokens</li><li>max: 67 tokens</li></ul> |
+  |         | sentence_0                                                                        | sentence_1                                                                       | sentence_2                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | string                                                                             |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 15.53 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.5 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.87 tokens</li><li>max: 128 tokens</li></ul> |
 * 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>What is the work done at Goldman Sachs?</code>                                           |
-  | <code>Will time travel be possible in future?</code>                                                                                                               | <code>Is time travel still theorized as being possible?</code>                                                                                                     | <code>What are the things that would make you fail a Canadian immigration medical exam?</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>What does it feel like to be eaten alive by a Pachycephalosaurus?</code>                 |
+  | sentence_0                                                                              | sentence_1                                                                              | sentence_2                                                             |
+  |:----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|
+  | <code>Is there visitor entry facility in Jaipur airport. How much is the ticket?</code> | <code>Is there visitor entry facility in Jaipur airport. How much is the ticket?</code> | <code>How much is the airport tax in bogota?</code>                    |
+  | <code>Which concept is more important: good planning or hard work?</code>               | <code>Which concept is more important: good planning or hard work?</code>               | <code>What is important in life: luck or hard work?</code>             |
+  | <code>What is the most efficient way to make money?</code>                              | <code>How can I make my money make money?</code>                                        | <code>What can one learn about Quantum Mechanics in 10 minutes?</code> |
 * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
   ```json
   {
@@ -294,49 +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-a-baseline
-- `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
@@ -364,14 +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
@@ -381,23 +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-a-baseline
+- `hub_model_id`: None
 - `hub_strategy`: every_save
 - `hub_private_repo`: None
 - `hub_always_push`: False
@@ -424,58 +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    | -             | 1.6082          | 0.8775             |
-| 0.2849 | 100  | 1.2224        | 0.1085          | 0.9385             |
-| 0.5698 | 200  | 0.181         | 0.0711          | 0.9484             |
-| 0.8547 | 300  | 0.1372        | 0.0593          | 0.9521             |
-| 1.1396 | 400  | 0.1161        | 0.0548          | 0.9524             |
-| 1.4245 | 500  | 0.1005        | 0.0516          | 0.9535             |
-| 1.7094 | 600  | 0.1023        | 0.0491          | 0.9545             |
-| 1.9943 | 700  | 0.0885        | 0.0469          | 0.9556             |
-| 2.2792 | 800  | 0.0836        | 0.0453          | 0.9552             |
-| 2.5641 | 900  | 0.0782        | 0.0439          | 0.9562             |
-| 2.8490 | 1000 | 0.0745        | 0.0436          | 0.9572             |
-| 3.1339 | 1100 | 0.0732        | 0.0421          | 0.9570             |
-| 3.4188 | 1200 | 0.0688        | 0.0417          | 0.9577             |
-| 3.7037 | 1300 | 0.0687        | 0.0411          | 0.9576             |
-| 3.9886 | 1400 | 0.07          | 0.0412          | 0.9573             |
-| 4.2735 | 1500 | 0.0635        | 0.0402          | 0.9578             |
-| 4.5584 | 1600 | 0.0638        | 0.0397          | 0.9575             |
-| 4.8433 | 1700 | 0.0613        | 0.0394          | 0.9579             |
-| 5.1282 | 1800 | 0.0625        | 0.0388          | 0.9584             |
-| 5.4131 | 1900 | 0.0585        | 0.0382          | 0.9586             |
-| 5.6980 | 2000 | 0.0594        | 0.0379          | 0.9585             |
-| 5.9829 | 2100 | 0.0566        | 0.0377          | 0.9584             |
-| 6.2678 | 2200 | 0.0545        | 0.0376          | 0.9583             |
-| 6.5527 | 2300 | 0.0535        | 0.0376          | 0.9580             |
-| 6.8376 | 2400 | 0.0573        | 0.0373          | 0.9584             |
-| 7.1225 | 2500 | 0.0528        | 0.0373          | 0.9583             |
-| 7.4074 | 2600 | 0.053         | 0.0371          | 0.9587             |
-| 7.6923 | 2700 | 0.0528        | 0.0368          | 0.9587             |
-| 7.9772 | 2800 | 0.0531        | 0.0366          | 0.9585             |
-| 8.2621 | 2900 | 0.0532        | 0.0365          | 0.9586             |
-| 8.5470 | 3000 | 0.0516        | 0.0365          | 0.9584             |
-| 8.8319 | 3100 | 0.0509        | 0.0364          | 0.9585             |
-| 9.1168 | 3200 | 0.0544        | 0.0363          | 0.9587             |
-| 9.4017 | 3300 | 0.0505        | 0.0364          | 0.9585             |
-| 9.6866 | 3400 | 0.052         | 0.0363          | 0.9587             |
-| 9.9715 | 3500 | 0.0536        | 0.0362          | 0.9586             |
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.3199 | 500  | 0.2284        |
+| 0.6398 | 1000 | 0.0571        |
+| 0.9597 | 1500 | 0.0486        |
+| 1.2796 | 2000 | 0.0378        |
+| 1.5995 | 2500 | 0.0367        |
+| 1.9194 | 3000 | 0.0338        |
+| 2.2393 | 3500 | 0.0327        |
+| 2.5592 | 4000 | 0.0285        |
+| 2.8791 | 4500 | 0.0285        |
 
 
 ### Framework Versions

eval/Information-Retrieval_evaluation_val_results.csv

@@ -38,3 +38,145 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Precisi
 9.401709401709402,3300,0.918,0.9702,0.9854,0.918,0.918,0.3234,0.9702,0.19707999999999998,0.9854,0.918,0.9449833333333331,0.9464248412698404,0.9585364250732368,0.9467604500159339
 9.686609686609687,3400,0.9184,0.9702,0.9852,0.9184,0.9184,0.3234,0.9702,0.19703999999999997,0.9852,0.9184,0.9451433333333329,0.946628888888888,0.9586880746687382,0.9469662432012432
 9.971509971509972,3500,0.9184,0.97,0.9852,0.9184,0.9184,0.3233333333333333,0.97,0.19703999999999997,0.9852,0.9184,0.9451033333333331,0.9465657142857136,0.9585962869405669,0.9469212791024237
+0,0,0.755775,0.808875,0.831025,0.755775,0.755775,0.269625,0.808875,0.16620500000000002,0.831025,0.755775,0.7844620833333302,0.7884356150793639,0.8057171258182194,0.7915587339356275
+0,0,0.7547,0.807275,0.83055,0.7547,0.7547,0.2690916666666667,0.807275,0.16611,0.83055,0.7547,0.7832520833333291,0.7872015575396802,0.8045860339061293,0.7903241559279329
+0.07112375533428165,100,0.78785,0.851075,0.875425,0.78785,0.78785,0.2836916666666666,0.851075,0.17508500000000002,0.875425,0.78785,0.8214812499999945,0.8259418948412669,0.845761155370626,0.8289483897643156
+0.1422475106685633,200,0.807425,0.8746,0.8983,0.807425,0.807425,0.2915333333333333,0.8746,0.17966000000000001,0.8983,0.807425,0.8427741666666617,0.8470684722222184,0.8670382366177412,0.8497105310760685
+0.21337126600284495,300,0.81345,0.881175,0.906,0.81345,0.81345,0.293725,0.881175,0.1812,0.906,0.81345,0.8493633333333297,0.853414712301584,0.8733459236763736,0.856061621034904
+0.2844950213371266,400,0.81635,0.88395,0.9094,0.81635,0.81635,0.2946499999999999,0.88395,0.18188000000000001,0.9094,0.81635,0.8522504166666626,0.8563771230158682,0.8765756531000587,0.8589065085962513
+0.35561877667140823,500,0.818,0.886175,0.911175,0.818,0.818,0.2953916666666666,0.886175,0.18223500000000004,0.911175,0.818,0.8539554166666614,0.8580862499999943,0.8782710147523383,0.860644702658202
+0.4267425320056899,600,0.819475,0.887525,0.91275,0.819475,0.819475,0.2958416666666666,0.887525,0.18255000000000002,0.91275,0.819475,0.8554229166666614,0.8595847023809465,0.8798219435803046,0.8621275028888417
+0.49786628733997157,700,0.820625,0.888875,0.9142,0.820625,0.820625,0.2962916666666667,0.888875,0.18284,0.9142,0.820625,0.856661666666661,0.860812668650788,0.8810357441052341,0.8633834646157147
+0.5689900426742532,800,0.820925,0.8898,0.916,0.820925,0.820925,0.2966,0.8898,0.1832,0.916,0.820925,0.8574945833333277,0.8615348710317401,0.881842076773151,0.8640919041835208
+0.6401137980085349,900,0.82165,0.890475,0.91665,0.82165,0.82165,0.29682499999999995,0.890475,0.18333,0.91665,0.82165,0.8582687499999951,0.8624309920634873,0.8828656071112492,0.8649659283844867
+0.7112375533428165,1000,0.822125,0.890975,0.9178,0.822125,0.822125,0.2969916666666666,0.890975,0.18356000000000003,0.9178,0.822125,0.8588987499999945,0.8629843452380895,0.8834907335792129,0.865520857294089
+0.7823613086770982,1100,0.8221,0.892,0.918775,0.8221,0.8221,0.2973333333333333,0.892,0.18375500000000003,0.918775,0.8221,0.8593183333333276,0.8634522321428502,0.8841758741095582,0.8659255405241576
+0.8534850640113798,1200,0.822275,0.8923,0.919725,0.822275,0.822275,0.2974333333333333,0.8923,0.18394500000000003,0.919725,0.822275,0.8597520833333285,0.8637605456349143,0.8844333496944642,0.8663024863202818
+0.9246088193456614,1300,0.823225,0.893525,0.920575,0.823225,0.823225,0.29784166666666667,0.893525,0.18411500000000003,0.920575,0.823225,0.8606774999999949,0.8647051984126934,0.8854128554093778,0.867209868995025
+0.9957325746799431,1400,0.823175,0.894025,0.921075,0.823175,0.823175,0.2980083333333333,0.894025,0.18421500000000005,0.921075,0.823175,0.860848333333329,0.8649403769841214,0.8858600106323372,0.867393318084569
+1.0668563300142249,1500,0.82355,0.894475,0.921425,0.82355,0.82355,0.2981583333333333,0.894475,0.18428500000000003,0.921425,0.82355,0.8612766666666617,0.8654512003968201,0.8864450013473706,0.8678860308032963
+1.1379800853485065,1600,0.824075,0.893975,0.92145,0.824075,0.824075,0.2979916666666666,0.893975,0.18429000000000006,0.92145,0.824075,0.8616245833333293,0.8658359027777728,0.8867900484928938,0.8682721259875404
+1.209103840682788,1700,0.8243,0.89475,0.922,0.8243,0.8243,0.29824999999999996,0.89475,0.18440000000000004,0.922,0.8243,0.8618979166666617,0.8660758134920578,0.8870159340452088,0.8685273486995436
+1.2802275960170697,1800,0.8239,0.894975,0.922625,0.8239,0.8239,0.2983249999999999,0.894975,0.184525,0.922625,0.8239,0.8619162499999955,0.8660865972222171,0.8871502465991052,0.868526031193211
+1.3513513513513513,1900,0.8248,0.8953,0.92285,0.8248,0.8248,0.29843333333333333,0.8953,0.18457000000000004,0.92285,0.8248,0.8624554166666616,0.8667771626984062,0.887979642622687,0.8691551061429417
+1.422475106685633,2000,0.825125,0.895675,0.92305,0.825125,0.825125,0.2985583333333333,0.895675,0.18461000000000002,0.92305,0.825125,0.8627966666666622,0.8671086408730106,0.8882894213555926,0.8694945359957533
+1.4935988620199145,2100,0.82465,0.895475,0.9231,0.82465,0.82465,0.29849166666666666,0.895475,0.18462000000000003,0.9231,0.82465,0.8625087499999958,0.8667821329365039,0.8879802791439102,0.8691910176083405
+1.5647226173541964,2200,0.824775,0.8964,0.92295,0.824775,0.824775,0.29879999999999995,0.8964,0.18459000000000006,0.92295,0.824775,0.8627320833333293,0.8670929067460275,0.8883622448503676,0.8694565192049544
+1.635846372688478,2300,0.8248,0.896275,0.923925,0.8248,0.8248,0.2987583333333333,0.896275,0.18478500000000003,0.923925,0.8248,0.8630224999999968,0.8673088095238061,0.8886264876214104,0.8696932008768681
+1.7069701280227596,2400,0.826025,0.89705,0.92455,0.826025,0.826025,0.29901666666666665,0.89705,0.18491,0.92455,0.826025,0.8638491666666629,0.8681291865079321,0.8894101179460802,0.87048095001459
+1.7780938833570412,2500,0.82575,0.89745,0.924525,0.82575,0.82575,0.29914999999999997,0.89745,0.184905,0.924525,0.82575,0.863837499999996,0.8681003472222181,0.8893350803981342,0.8704980090657853
+1.8492176386913228,2600,0.8262,0.89755,0.9245,0.8262,0.8262,0.29918333333333325,0.89755,0.18490000000000004,0.9245,0.8262,0.8639870833333294,0.8682846924603129,0.8895707677394977,0.8706525163734248
+1.9203413940256047,2700,0.825625,0.897375,0.924725,0.825625,0.825625,0.299125,0.897375,0.18494500000000003,0.924725,0.825625,0.8637699999999955,0.8680662599206297,0.8894356770626558,0.8704346187020383
+1.991465149359886,2800,0.8265,0.898175,0.9257,0.8265,0.8265,0.2993916666666666,0.898175,0.18514,0.9257,0.8265,0.8645654166666631,0.8688316964285668,0.8902285640417104,0.871149650917906
+2.062588904694168,2900,0.82645,0.898625,0.92595,0.82645,0.82645,0.2995416666666666,0.898625,0.18519000000000002,0.92595,0.82645,0.8647274999999957,0.8689719345238045,0.890342026566598,0.871310932644214
+2.1337126600284497,3000,0.826425,0.899225,0.9261,0.826425,0.826425,0.29974166666666663,0.899225,0.18522000000000002,0.9261,0.826425,0.8647395833333298,0.8689735416666621,0.890347823617768,0.8713261595062335
+2.204836415362731,3100,0.826725,0.89905,0.92645,0.826725,0.826725,0.29968333333333325,0.89905,0.18529,0.92645,0.826725,0.8649899999999954,0.8692784920634858,0.8908060838763998,0.871566509775546
+2.275960170697013,3200,0.8269,0.899,0.92655,0.8269,0.8269,0.2996666666666666,0.899,0.18531000000000003,0.92655,0.8269,0.8651883333333286,0.8694821428571373,0.8909713514566135,0.871765747281156
+2.3470839260312943,3300,0.8271,0.8991,0.92655,0.8271,0.8271,0.2997,0.8991,0.18531,0.92655,0.8271,0.8653516666666632,0.8695972619047567,0.8909526338857612,0.8719357528477157
+2.418207681365576,3400,0.826625,0.8992,0.926825,0.826625,0.826625,0.2997333333333333,0.8992,0.185365,0.926825,0.826625,0.8650733333333291,0.8692719940476129,0.8907103492984229,0.8716175978469811
+2.4893314366998576,3500,0.826775,0.899325,0.926875,0.826775,0.826775,0.2997749999999999,0.899325,0.185375,0.926875,0.826775,0.8652879166666612,0.8695422321428495,0.8909951161321312,0.8718809967982727
+2.5604551920341394,3600,0.827025,0.89965,0.926925,0.827025,0.827025,0.2998833333333333,0.89965,0.18538500000000005,0.926925,0.827025,0.8655404166666618,0.8698209027777717,0.8912443530415479,0.8721588161360633
+2.6315789473684212,3700,0.8272,0.899875,0.92655,0.8272,0.8272,0.29995833333333327,0.899875,0.18531000000000003,0.92655,0.8272,0.8656041666666623,0.8700131448412636,0.8914837712801771,0.8723314630431057
+2.7027027027027026,3800,0.8266,0.899825,0.927175,0.8266,0.8266,0.29994166666666666,0.899825,0.18543500000000004,0.927175,0.8266,0.8653658333333281,0.8696869841269774,0.8913161563114452,0.8719612942855474
+2.7738264580369845,3900,0.8272,0.899825,0.92715,0.8272,0.8272,0.2999416666666666,0.899825,0.18543,0.92715,0.8272,0.8657754166666611,0.8701063789682477,0.8916378283367434,0.8723927691710214
+2.844950213371266,4000,0.827075,0.89955,0.92675,0.827075,0.827075,0.29984999999999995,0.89955,0.18535000000000001,0.92675,0.827075,0.8655716666666616,0.8699127083333278,0.8913728457986007,0.8722460602722183
+2.9160739687055477,4100,0.8273,0.900125,0.9276,0.8273,0.8273,0.3000416666666666,0.900125,0.18552000000000002,0.9276,0.8273,0.8659283333333285,0.870234494047613,0.8917878356256379,0.8725341820908176
+2.987197724039829,4200,0.82765,0.899675,0.928275,0.82765,0.82765,0.29989166666666667,0.899675,0.18565500000000001,0.928275,0.82765,0.8661887499999953,0.8704055357142805,0.8919296060213343,0.8727003846925537
+3.058321479374111,4300,0.827625,0.900125,0.92825,0.827625,0.827625,0.3000416666666666,0.900125,0.18565000000000004,0.92825,0.827625,0.8662208333333287,0.8705009821428515,0.8921252154319398,0.8727522181409114
+3.1294452347083928,4400,0.828,0.900825,0.928325,0.828,0.828,0.300275,0.900825,0.18566500000000002,0.928325,0.828,0.8665966666666624,0.8708945734126934,0.8924466951830272,0.8731530635038529
+3.200568990042674,4500,0.8277,0.9,0.92835,0.8277,0.8277,0.29999999999999993,0.9,0.18567000000000003,0.92835,0.8277,0.866319999999995,0.8706282341269782,0.8922483527994851,0.8728924711403877
+3.271692745376956,4600,0.82805,0.90105,0.92865,0.82805,0.82805,0.30035,0.90105,0.18573,0.92865,0.82805,0.8666904166666612,0.8709508432539609,0.8925163619705987,0.8732096836080842
+3.3428165007112374,4700,0.8278,0.900575,0.92825,0.8278,0.8278,0.3001916666666666,0.900575,0.18565,0.92825,0.8278,0.8665091666666603,0.8708543650793578,0.8924746282919447,0.8731299308768117
+3.413940256045519,4800,0.827825,0.900525,0.92835,0.827825,0.827825,0.30017499999999997,0.900525,0.18567000000000003,0.92835,0.827825,0.8665587499999943,0.8708749603174522,0.8925184426900059,0.8731293966791309
+3.485064011379801,4900,0.8282,0.901025,0.9294,0.8282,0.8282,0.3003416666666666,0.901025,0.18588000000000002,0.9294,0.8282,0.8669949999999941,0.8712464583333255,0.8929341649361835,0.873471354856535
+3.5561877667140824,5000,0.82795,0.90165,0.928775,0.82795,0.82795,0.30055,0.90165,0.18575500000000003,0.928775,0.82795,0.8668574999999944,0.8712117658730092,0.8929485306096925,0.8734092206180317
+3.6273115220483643,5100,0.82795,0.90155,0.9291,0.82795,0.82795,0.30051666666666665,0.90155,0.18582000000000004,0.9291,0.82795,0.8669424999999945,0.8712447321428517,0.8929399616791724,0.8734732719376523
+3.6984352773826457,5200,0.82825,0.901425,0.929075,0.82825,0.82825,0.30047499999999994,0.901425,0.185815,0.929075,0.82825,0.8670362499999954,0.8712648313492005,0.8928164376378238,0.8735312781146788
+3.7695590327169275,5300,0.827725,0.901425,0.92925,0.827725,0.827725,0.30047499999999994,0.901425,0.18585000000000002,0.92925,0.827725,0.8668066666666613,0.8710934523809452,0.8928289077266707,0.873324647003722
+3.8406827880512093,5400,0.827825,0.90145,0.929,0.827825,0.827825,0.3004833333333333,0.90145,0.18580000000000002,0.929,0.827825,0.8668691666666618,0.871203353174598,0.8928865801326494,0.8734427586278187
+3.9118065433854907,5500,0.828325,0.90155,0.929,0.828325,0.828325,0.30051666666666665,0.90155,0.18580000000000002,0.929,0.828325,0.8671308333333289,0.8714839384920581,0.8931380829352583,0.87370417401581
+3.9829302987197726,5600,0.827875,0.901325,0.929625,0.827875,0.827875,0.3004416666666666,0.901325,0.18592500000000003,0.929625,0.827875,0.8670637499999959,0.8713995535714232,0.8932282155573206,0.8735739145143638
+4.054054054054054,5700,0.828225,0.901825,0.92965,0.828225,0.828225,0.3006083333333333,0.901825,0.18592999999999998,0.92965,0.828225,0.8672995833333278,0.8715914484126921,0.8933318061811949,0.8737879900726819
+4.125177809388336,5800,0.828125,0.902575,0.9298,0.828125,0.828125,0.3008583333333333,0.902575,0.18596000000000004,0.9298,0.828125,0.8674095833333273,0.871657361111104,0.8933427952172275,0.8738836312779211
+4.196301564722617,5900,0.8281,0.901525,0.930075,0.8281,0.8281,0.30050833333333327,0.901525,0.18601500000000004,0.930075,0.8281,0.8673141666666621,0.8715502579365025,0.8932912071203153,0.8737613329234138
+4.2674253200568995,6000,0.8285,0.90215,0.9301,0.8285,0.8285,0.30071666666666663,0.90215,0.18602000000000002,0.9301,0.8285,0.8677162499999951,0.8719605952380887,0.8935923803057578,0.8741910760623423
+4.338549075391181,6100,0.828225,0.9024,0.929725,0.828225,0.828225,0.3008,0.9024,0.185945,0.929725,0.828225,0.8673716666666614,0.8717138293650726,0.8934810443914722,0.873907773748694
+4.409672830725462,6200,0.828075,0.902125,0.929625,0.828075,0.828075,0.3007083333333333,0.902125,0.18592500000000003,0.929625,0.828075,0.8672837499999961,0.8716056646825336,0.8933245645232728,0.8738314386660578
+4.480796586059744,6300,0.8285,0.902475,0.930375,0.8285,0.8285,0.300825,0.902475,0.186075,0.930375,0.8285,0.8677258333333291,0.8719892063492011,0.8937460890492909,0.8741866281708112
+4.551920341394026,6400,0.829025,0.9031,0.930125,0.829025,0.829025,0.3010333333333333,0.9031,0.18602500000000005,0.930125,0.829025,0.868045833333328,0.8723535912698358,0.8939942369427725,0.8745777316869345
+4.623044096728307,6500,0.828925,0.902675,0.93025,0.828925,0.828925,0.3008916666666666,0.902675,0.18605000000000002,0.93025,0.828925,0.8679429166666623,0.8722636011904705,0.894011390451915,0.8744507896495303
+4.694167852062589,6600,0.828775,0.902675,0.930525,0.828775,0.828775,0.3008916666666666,0.902675,0.18610500000000002,0.930525,0.828775,0.8679779166666611,0.872248293650788,0.8939521912453529,0.874467781370097
+4.76529160739687,6700,0.82895,0.90245,0.93035,0.82895,0.82895,0.3008166666666666,0.90245,0.18607000000000004,0.93035,0.82895,0.8679879166666622,0.8723038789682495,0.8940473756170835,0.8744915171684532
+4.836415362731152,6800,0.8283,0.90305,0.9309,0.8283,0.8283,0.3010166666666666,0.90305,0.18618,0.9309,0.8283,0.867910416666662,0.8721540773809471,0.8939497437147061,0.8743487656718353
+4.907539118065434,6900,0.828325,0.902925,0.93045,0.828325,0.828325,0.30097499999999994,0.902925,0.18609000000000003,0.93045,0.828325,0.8678854166666622,0.8721938888888839,0.8939514220767489,0.8744030273602739
+4.978662873399715,7000,0.8283,0.903225,0.930375,0.8283,0.8283,0.301075,0.903225,0.18607500000000002,0.930375,0.8283,0.8678333333333287,0.8721642956349152,0.8939665996423323,0.8743696253316319
+5.049786628733997,7100,0.827825,0.9032,0.930075,0.827825,0.827825,0.3010666666666666,0.9032,0.186015,0.930075,0.827825,0.8675479166666622,0.8719574007936451,0.8938974126211886,0.8741217627464118
+5.120910384068279,7200,0.828225,0.903075,0.93035,0.828225,0.828225,0.30102499999999993,0.903075,0.18607000000000004,0.93035,0.828225,0.8677999999999951,0.8721783134920578,0.894072625572504,0.8743447352455296
+5.19203413940256,7300,0.827875,0.903275,0.93085,0.827875,0.827875,0.3010916666666666,0.903275,0.18617000000000003,0.93085,0.827875,0.8677724999999952,0.8720725595238032,0.893982576896927,0.8742551897684425
+5.2631578947368425,7400,0.82855,0.903125,0.930825,0.82855,0.82855,0.30104166666666665,0.903125,0.186165,0.930825,0.82855,0.8680941666666616,0.8724458630952325,0.8943261872169521,0.8746091010205986
+5.334281650071124,7500,0.8282,0.9027,0.9307,0.8282,0.8282,0.3009,0.9027,0.18614000000000003,0.9307,0.8282,0.8677720833333288,0.8721882242063433,0.8942269763046193,0.8743096835645798
+5.405405405405405,7600,0.828575,0.90245,0.930475,0.828575,0.828575,0.3008166666666667,0.90245,0.18609500000000004,0.930475,0.828575,0.8679016666666634,0.8722942460317409,0.894169304471156,0.8744688088000298
+5.476529160739687,7700,0.82835,0.90285,0.9306,0.82835,0.82835,0.3009499999999999,0.90285,0.18612000000000004,0.9306,0.82835,0.8678508333333291,0.8722199404761842,0.8941142458749253,0.8744018627520107
+5.547652916073969,7800,0.828625,0.903475,0.930225,0.828625,0.828625,0.3011583333333333,0.903475,0.18604500000000002,0.930225,0.828625,0.8680270833333281,0.8724584722222167,0.8942713349975829,0.8746604618279742
+5.61877667140825,7900,0.82875,0.902825,0.930875,0.82875,0.82875,0.3009416666666666,0.902825,0.186175,0.930875,0.82875,0.8681183333333286,0.8724924107142793,0.8944002954247615,0.8746483877607266
+5.689900426742532,8000,0.82915,0.903525,0.93085,0.82915,0.82915,0.301175,0.903525,0.18617000000000003,0.93085,0.82915,0.8684824999999949,0.8728611805555482,0.8947027956822773,0.8750076119907729
+5.761024182076814,8100,0.829025,0.902775,0.9308,0.829025,0.829025,0.3009249999999999,0.902775,0.18616000000000005,0.9308,0.829025,0.8682724999999946,0.8727015178571357,0.8946280631253088,0.8748339744524721
+5.832147937411095,8200,0.8286,0.903225,0.9308,0.8286,0.8286,0.3010749999999999,0.903225,0.18616000000000002,0.9308,0.8286,0.8680666666666624,0.8724622420634867,0.8944261476913996,0.8745954232847529
+5.903271692745377,8300,0.82895,0.903725,0.930775,0.82895,0.82895,0.30124166666666663,0.903725,0.18615500000000001,0.930775,0.82895,0.8683333333333292,0.8727454464285668,0.8946178536849202,0.8748963303338884
+5.974395448079658,8400,0.8288,0.9036,0.9312,0.8288,0.8288,0.3011999999999999,0.9036,0.18624000000000004,0.9312,0.8288,0.8683545833333292,0.8727151686507881,0.8946702577402386,0.8748394145685222
+6.0455192034139404,8500,0.829175,0.90385,0.93095,0.829175,0.829175,0.30128333333333324,0.90385,0.18619000000000002,0.93095,0.829175,0.8685420833333285,0.872971696428565,0.8948846695179693,0.8750987853504063
+6.116642958748222,8600,0.829,0.903525,0.9313,0.829,0.829,0.301175,0.903525,0.18626000000000004,0.9313,0.829,0.8685441666666623,0.8729358035714229,0.8949065301167233,0.87504230073798
+6.187766714082503,8700,0.829075,0.903825,0.931125,0.829075,0.829075,0.301275,0.903825,0.18622500000000003,0.931125,0.829075,0.8685433333333281,0.8729486210317393,0.894908715557474,0.8750547367584172
+6.2588904694167855,8800,0.8294,0.9034,0.931025,0.8294,0.8294,0.3011333333333333,0.9034,0.186205,0.931025,0.8294,0.8687245833333284,0.8731362599206285,0.8950249083831672,0.8752570268200927
+6.330014224751067,8900,0.8291,0.904175,0.93095,0.8291,0.8291,0.3013916666666666,0.904175,0.18619,0.93095,0.8291,0.8686062499999953,0.873074950396819,0.8950197884709341,0.875183958791522
+6.401137980085348,9000,0.829075,0.903525,0.9311,0.829075,0.829075,0.30117499999999997,0.903525,0.18622000000000002,0.9311,0.829075,0.8684637499999962,0.8728336607142791,0.8947201341464351,0.8749830024617687
+6.472261735419631,9100,0.82895,0.903925,0.931125,0.82895,0.82895,0.3013083333333333,0.903925,0.18622500000000006,0.931125,0.82895,0.868523749999995,0.8729523313491995,0.8949264125232629,0.8750632591156137
+6.543385490753912,9200,0.8287,0.903875,0.931525,0.8287,0.8287,0.3012916666666666,0.903875,0.18630500000000003,0.931525,0.8287,0.8685312499999951,0.8728960416666594,0.8948964473390973,0.8750060398957655
+6.614509246088193,9300,0.828575,0.9037,0.930975,0.828575,0.828575,0.30123333333333324,0.9037,0.18619500000000003,0.930975,0.828575,0.8682566666666615,0.8727102876984051,0.8947419499833495,0.8748258780472974
+6.685633001422475,9400,0.828725,0.903625,0.9312,0.828725,0.828725,0.3012083333333333,0.903625,0.18624000000000004,0.9312,0.828725,0.8684295833333284,0.8728350694444372,0.8948174140374873,0.8749621158488419
+6.756756756756757,9500,0.828925,0.903825,0.9316,0.828925,0.828925,0.30127499999999996,0.903825,0.18632000000000004,0.9316,0.828925,0.8686979166666619,0.8730864484126918,0.8950858390267745,0.875175061860045
+6.827880512091038,9600,0.828925,0.904025,0.931325,0.828925,0.828925,0.3013416666666666,0.904025,0.18626500000000004,0.931325,0.828925,0.8686649999999941,0.8730839285714214,0.8950756231504231,0.8751769766045896
+6.89900426742532,9700,0.829325,0.90415,0.931525,0.829325,0.829325,0.3013833333333333,0.90415,0.18630500000000003,0.931525,0.829325,0.8689170833333283,0.8733062301587239,0.8952108076269908,0.8754240592488881
+6.970128022759602,9800,0.829225,0.9039,0.9316,0.829225,0.829225,0.3013,0.9039,0.18632000000000004,0.9316,0.829225,0.8688504166666607,0.8732341468253897,0.8951715488614492,0.8753505377411699
+7.0412517780938835,9900,0.828825,0.9036,0.931525,0.828825,0.828825,0.30119999999999997,0.9036,0.18630500000000003,0.931525,0.828825,0.8685466666666609,0.8729646825396757,0.8950075880619315,0.8750548303490677
+7.112375533428165,10000,0.829125,0.903825,0.9317,0.829125,0.829125,0.301275,0.903825,0.18634,0.9317,0.829125,0.8687299999999943,0.8731202976190414,0.8951116703189326,0.8752221802611312
+7.183499288762446,10100,0.8292,0.90395,0.932,0.8292,0.8292,0.3013166666666666,0.90395,0.18640000000000004,0.932,0.8292,0.8688920833333279,0.8732386111111037,0.8952233766688119,0.8753352290567349
+7.2546230440967285,10200,0.829225,0.90445,0.93195,0.829225,0.829225,0.3014833333333333,0.90445,0.18639,0.93195,0.829225,0.8689337499999943,0.87331557539682,0.8953072675203337,0.8754143962131168
+7.32574679943101,10300,0.828975,0.9043,0.9317,0.828975,0.828975,0.3014333333333333,0.9043,0.18634000000000003,0.9317,0.828975,0.8687420833333279,0.8731662003968191,0.8952068599802827,0.875254118597502
+7.396870554765291,10400,0.829125,0.903825,0.932025,0.829125,0.829125,0.30127499999999996,0.903825,0.18640500000000002,0.932025,0.829125,0.8687904166666616,0.8731562202380894,0.8951799564271696,0.8752576132029881
+7.467994310099574,10500,0.829125,0.90375,0.9321,0.829125,0.829125,0.30124999999999996,0.90375,0.18642000000000003,0.9321,0.829125,0.8687862499999952,0.8731379265872954,0.8951780327010416,0.8752325887196015
+7.539118065433855,10600,0.82935,0.904225,0.932225,0.82935,0.82935,0.3014083333333333,0.904225,0.18644500000000003,0.932225,0.82935,0.8689916666666612,0.8733285813491991,0.8953223283998646,0.8754304197917856
+7.610241820768136,10700,0.828875,0.904125,0.9319,0.828875,0.828875,0.30137499999999995,0.904125,0.18638000000000002,0.9319,0.828875,0.8686462499999948,0.8730549603174542,0.8951443508540531,0.8751426179544144
+7.681365576102419,10800,0.8294,0.90425,0.932125,0.8294,0.8294,0.3014166666666666,0.90425,0.18642500000000004,0.932125,0.8294,0.8689254166666617,0.8733101587301526,0.8953412645129609,0.8753982744981171
+7.7524893314367,10900,0.829525,0.904075,0.931875,0.829525,0.829525,0.3013583333333333,0.904075,0.18637500000000004,0.931875,0.829525,0.869002916666662,0.8734263988095179,0.8954372160730756,0.87550507311346
+7.823613086770981,11000,0.829225,0.904325,0.93195,0.829225,0.829225,0.30144166666666666,0.904325,0.18639000000000003,0.93195,0.829225,0.8689704166666619,0.8734005753968189,0.8954592783354139,0.875473740734129
+7.894736842105263,11100,0.829375,0.904325,0.931975,0.829375,0.829375,0.3014416666666666,0.904325,0.18639500000000003,0.931975,0.829375,0.8690224999999943,0.8734514186507863,0.8954979567496186,0.8755274020925966
+7.965860597439545,11200,0.82955,0.9045,0.93215,0.82955,0.82955,0.30149999999999993,0.9045,0.18643,0.93215,0.82955,0.8692179166666613,0.8736397519841202,0.8956820121323833,0.8757002350962841
+8.036984352773827,11300,0.82955,0.904175,0.9324,0.82955,0.82955,0.30139166666666667,0.904175,0.18648,0.9324,0.82955,0.8691774999999948,0.8735149603174545,0.8955172674364549,0.8756027777748313
+8.108108108108109,11400,0.829325,0.90435,0.932425,0.829325,0.829325,0.30144999999999994,0.90435,0.18648500000000004,0.932425,0.829325,0.8691216666666615,0.8734770238095176,0.8955123296939637,0.8755540663940554
+8.17923186344239,11500,0.82905,0.9043,0.9326,0.82905,0.82905,0.3014333333333333,0.9043,0.18652000000000002,0.9326,0.82905,0.8689616666666612,0.8733028571428504,0.8954049605787512,0.8753753151688954
+8.250355618776672,11600,0.829575,0.904075,0.93235,0.829575,0.829575,0.3013583333333333,0.904075,0.18647000000000002,0.93235,0.829575,0.8692079166666619,0.8735856150793588,0.8955883966928999,0.8756651485608432
+8.321479374110954,11700,0.829,0.90435,0.932375,0.829,0.829,0.30144999999999994,0.90435,0.186475,0.932375,0.829,0.8689162499999953,0.8733047916666604,0.8954331340853403,0.8753585929214951
+8.392603129445234,11800,0.82955,0.9045,0.932425,0.82955,0.82955,0.3015,0.9045,0.18648500000000004,0.932425,0.82955,0.8692320833333294,0.8736002976190421,0.8956159725223579,0.8756756360478772
+8.463726884779517,11900,0.829575,0.90465,0.932525,0.829575,0.829575,0.30154999999999993,0.90465,0.18650500000000003,0.932525,0.829575,0.8693274999999956,0.8736795238095177,0.8957038979386316,0.8757370242050787
+8.534850640113799,12000,0.8295,0.90455,0.932525,0.8295,0.8295,0.30151666666666666,0.90455,0.18650500000000003,0.932525,0.8295,0.8693037499999957,0.8736758134920576,0.8957443910788215,0.8757191218855241
+8.60597439544808,12100,0.829375,0.9046,0.932475,0.829375,0.829375,0.3015333333333333,0.9046,0.18649500000000002,0.932475,0.829375,0.869249999999996,0.8736443948412647,0.8957296640580314,0.8756884601745238
+8.677098150782362,12200,0.829675,0.904425,0.9326,0.829675,0.829675,0.301475,0.904425,0.18652000000000002,0.9326,0.829675,0.8693591666666626,0.8736971329365028,0.8957079678384477,0.8757613361872285
+8.748221906116642,12300,0.829675,0.90445,0.93235,0.829675,0.829675,0.3014833333333333,0.90445,0.18647,0.93235,0.829675,0.8693087499999952,0.8737007142857086,0.895718161327572,0.8757683518013507
+8.819345661450924,12400,0.8295,0.904375,0.932575,0.8295,0.8295,0.30145833333333333,0.904375,0.18651500000000001,0.932575,0.8295,0.8692854166666626,0.8736491765872961,0.8957043908845526,0.875705749877939
+8.890469416785207,12500,0.829525,0.904375,0.932475,0.829525,0.829525,0.3014583333333333,0.904375,0.18649500000000002,0.932475,0.829525,0.8692191666666625,0.8735937202380893,0.895662154246233,0.8756445157255779
+8.961593172119487,12600,0.829475,0.904575,0.9323,0.829475,0.829475,0.30152499999999993,0.904575,0.18646000000000004,0.9323,0.829475,0.8691791666666621,0.8736097519841208,0.8957145356259026,0.8756490977339323
+9.03271692745377,12700,0.82945,0.90425,0.932675,0.82945,0.82945,0.30141666666666667,0.90425,0.18653499999999998,0.932675,0.82945,0.869237916666663,0.8735875793650737,0.8956664069313527,0.8756409919548603
+9.103840682788052,12800,0.8294,0.90425,0.932675,0.8294,0.8294,0.30141666666666667,0.90425,0.18653500000000003,0.932675,0.8294,0.8691570833333291,0.8734929662698362,0.8955523610447522,0.8755622615987919
+9.174964438122332,12900,0.82915,0.904475,0.932675,0.82915,0.82915,0.3014916666666666,0.904475,0.186535,0.932675,0.82915,0.8690449999999957,0.8733805357142803,0.8954706666940537,0.8754538277866332
+9.246088193456615,13000,0.82935,0.904525,0.9326,0.82935,0.82935,0.3015083333333333,0.904525,0.18652000000000002,0.9326,0.82935,0.8691433333333289,0.873507013888883,0.8955913527336096,0.8755684032749883
+9.317211948790897,13100,0.8295,0.904475,0.932675,0.8295,0.8295,0.3014916666666666,0.904475,0.186535,0.932675,0.8295,0.869243749999996,0.8735823710317403,0.8956395313586778,0.8756462995333785
+9.388335704125177,13200,0.829575,0.90465,0.9329,0.829575,0.829575,0.30155,0.90465,0.18658000000000002,0.9329,0.829575,0.869395416666663,0.8736798313492016,0.8956789415552683,0.8757611231012578
+9.45945945945946,13300,0.8294,0.904625,0.93245,0.8294,0.8294,0.3015416666666666,0.904625,0.18649000000000004,0.93245,0.8294,0.8691949999999963,0.8735664980158677,0.8956132086027158,0.8756355010782446
+9.530583214793742,13400,0.8295,0.904725,0.932475,0.8295,0.8295,0.3015749999999999,0.904725,0.18649500000000002,0.932475,0.8295,0.8692941666666627,0.873653789682535,0.8956503675255127,0.8757333507075223
+9.601706970128022,13500,0.829525,0.904725,0.932375,0.829525,0.829525,0.301575,0.904725,0.18647500000000003,0.932375,0.829525,0.8692958333333296,0.8736637499999952,0.8956465103594379,0.8757491390118639
+9.672830725462305,13600,0.82965,0.90465,0.9325,0.82965,0.82965,0.30154999999999993,0.90465,0.1865,0.9325,0.82965,0.8693666666666627,0.8737252380952334,0.8957081100848372,0.8758065761725513
+9.743954480796585,13700,0.8295,0.90465,0.9325,0.8295,0.8295,0.30155,0.90465,0.18650000000000003,0.9325,0.8295,0.8693074999999961,0.8736817162698364,0.895700486958084,0.8757526814508423
+9.815078236130867,13800,0.8296,0.904725,0.932475,0.8296,0.8296,0.301575,0.904725,0.18649500000000002,0.932475,0.8296,0.8693120833333294,0.8736897916666616,0.8957134946126672,0.8757585053341062
+9.88620199146515,13900,0.829475,0.904825,0.932425,0.829475,0.829475,0.3016083333333333,0.904825,0.186485,0.932425,0.829475,0.8692816666666627,0.8736694444444394,0.8957054405004242,0.875738183071688
+9.95732574679943,14000,0.829675,0.9048,0.93245,0.829675,0.829675,0.3016,0.9048,0.18649000000000004,0.93245,0.829675,0.8693824999999958,0.8737714285714238,0.8957919450437679,0.8758361833602419

final_metrics.json

@@ -1,16 +1,16 @@
 {
-  "val_cosine_accuracy@1": 0.9208,
-  "val_cosine_accuracy@3": 0.9698,
-  "val_cosine_accuracy@5": 0.9842,
-  "val_cosine_precision@1": 0.9208,
-  "val_cosine_precision@3": 0.3232666666666667,
-  "val_cosine_precision@5": 0.19684,
-  "val_cosine_recall@1": 0.9208,
-  "val_cosine_recall@3": 0.9698,
-  "val_cosine_recall@5": 0.9842,
-  "val_cosine_ndcg@10": 0.9593212690041523,
-  "val_cosine_mrr@1": 0.9208,
-  "val_cosine_mrr@5": 0.9460899999999998,
-  "val_cosine_mrr@10": 0.9476021428571432,
-  "val_cosine_map@100": 0.9479260307963899
+  "val_cosine_accuracy@1": 0.9184,
+  "val_cosine_accuracy@3": 0.97,
+  "val_cosine_accuracy@5": 0.9852,
+  "val_cosine_precision@1": 0.9184,
+  "val_cosine_precision@3": 0.3233333333333333,
+  "val_cosine_precision@5": 0.19703999999999997,
+  "val_cosine_recall@1": 0.9184,
+  "val_cosine_recall@3": 0.97,
+  "val_cosine_recall@5": 0.9852,
+  "val_cosine_ndcg@10": 0.9586270476620361,
+  "val_cosine_mrr@1": 0.9184,
+  "val_cosine_mrr@5": 0.9451366666666663,
+  "val_cosine_mrr@10": 0.9466038095238087,
+  "val_cosine_map@100": 0.946959374340519
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3c69983267a6db5ae9801964f057ecf353f9ae2741d24a0d28d157e7ee25803a
+oid sha256:4ae394348a491d3e3613014bc35b25e1e57987b52b334312c28991a2034cb6a2
 size 114011616

training_args.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ddb13b4a71cfc0f5a0deb789459db15cf201f8e30be7b6d481e4467797f714fa
+oid sha256:767f865ad778a3cf6f884deef6446e71dd8ddc58b4697f307d5df2876e65b529
 size 6161