isolation-forest commited on
Commit
257cd82
1 Parent(s): 024d995

Add SetFit ABSA model

Browse files
README.md CHANGED
@@ -10,16 +10,18 @@ base_model: cointegrated/rubert-tiny2
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  metrics:
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  - accuracy
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  widget:
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- - text: Шеф - повар:Шеф - повар тоже с самого открытия .
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- - text: 'ресторана:Сомнений по поводу выбора ресторана на свадьбу не возникло , надеюсь
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- , что в самый важный день нашей жизни мы тоже останемся довольны , на этой неделе
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- идем заказывать : ) .'
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- - text: гребешки:Затем были гребешки вроде ничего , но отдавали уксусом , пюре вместе
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- с ним было пересолено .
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- - text: кафе:По кухне можно сказать , что это кафе для тех , кто любит соотношение
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- цены и качества .
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- - text: то:Я не ходила в этот ресторан в детстве , не знаю , как всё было когда -
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- то , но сейчас это вполне симпатичное и уютное заведение с хорошей кухней .
 
 
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  pipeline_tag: text-classification
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  inference: false
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  ---
@@ -45,7 +47,7 @@ This model was trained within the context of a larger system for ABSA, which loo
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  - **Model Type:** SetFit
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  - **Sentence Transformer body:** [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2)
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  - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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- - **spaCy Model:** en_core_web_lg
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  - **SetFitABSA Aspect Model:** [isolation-forest/setfit-absa-aspect](https://huggingface.co/isolation-forest/setfit-absa-aspect)
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  - **SetFitABSA Polarity Model:** [isolation-forest/setfit-absa-polarity](https://huggingface.co/isolation-forest/setfit-absa-polarity)
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  - **Maximum Sequence Length:** 2048 tokens
@@ -61,10 +63,10 @@ This model was trained within the context of a larger system for ABSA, which loo
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  - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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  ### Model Labels
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- | Label | Examples |
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- |:----------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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- | aspect | <ul><li>'Обслуживание:Обслуживание хорошее нас встретил метрдотель и провёл до столика который отлично нам подашел .'</li><li>'метрдотель:Обслуживание хорошее нас встретил метрдотель и провёл до столика который отлично нам подашел .'</li><li>'уголке:Он был в уютном уголке в конце главного зала , приглушенный свет это основная часть этого ресторана там нет дневного освещения это было большим плюсом для нашего дня рожденья !'</li></ul> |
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- | no aspect | <ul><li>'провёл до столика который отлично нам подашел:Обслуживание хорошее нас встретил метрдотель и провёл до столика который отлично нам подашел .'</li><li>'конце главного:Он был в уютном уголке в конце главного зала , приглушенный свет это основная часть этого ресторана там нет дневного освещения это было большим плюсом для нашего дня рожденья !'</li><li>'часть этого ресторана:Он был в уютном уголке в конце главного зала , приглушенный свет это основная часть этого ресторана там нет дневного освещения это было большим плюсом для нашего дня рожденья !'</li></ul> |
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  ## Uses
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@@ -119,11 +121,11 @@ preds = model("The food was great, but the venue is just way too busy.")
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  ### Training Set Metrics
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  | Training set | Min | Median | Max |
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  |:-------------|:----|:--------|:----|
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- | Word count | 3 | 32.2987 | 171 |
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  | Label | Training Sample Count |
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  |:----------|:----------------------|
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- | no aspect | 380 |
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  | aspect | 256 |
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  ### Training Hyperparameters
@@ -146,275 +148,889 @@ preds = model("The food was great, but the venue is just way too busy.")
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  ### Training Results
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  | Epoch | Step | Training Loss | Validation Loss |
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  |:------:|:-----:|:-------------:|:---------------:|
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- | 0.0001 | 1 | 0.2618 | - |
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- | 0.0038 | 50 | 0.2144 | - |
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- | 0.0076 | 100 | 0.2504 | - |
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- | 0.0114 | 150 | 0.2392 | - |
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- | 0.0152 | 200 | 0.2717 | - |
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- | 0.0190 | 250 | 0.2488 | - |
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- | 0.0228 | 300 | 0.2256 | - |
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- | 0.0266 | 350 | 0.2266 | - |
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- | 0.0304 | 400 | 0.2203 | - |
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- | 0.0342 | 450 | 0.2439 | - |
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- | 0.0380 | 500 | 0.2463 | - |
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- | 0.0418 | 550 | 0.3144 | - |
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- | 0.0456 | 600 | 0.1814 | - |
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- | 0.0494 | 650 | 0.1585 | - |
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- | 0.0532 | 700 | 0.0941 | - |
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- | 0.0570 | 750 | 0.1534 | - |
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- | 0.0608 | 800 | 0.0915 | - |
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- | 0.0646 | 850 | 0.1498 | - |
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- | 0.0684 | 900 | 0.0862 | - |
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- | 0.0722 | 950 | 0.0919 | - |
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- | 0.0760 | 1000 | 0.0252 | - |
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- | 0.0798 | 1050 | 0.0441 | - |
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- | 0.0836 | 1100 | 0.0808 | - |
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- | 0.0874 | 1150 | 0.1103 | - |
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- | 0.0912 | 1200 | 0.0138 | - |
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- | 0.0950 | 1250 | 0.052 | - |
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- | 0.0988 | 1300 | 0.0564 | - |
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- | 0.1026 | 1350 | 0.0058 | - |
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- | 0.1064 | 1400 | 0.0177 | - |
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- | 0.1102 | 1450 | 0.0651 | - |
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- | 0.1140 | 1500 | 0.0046 | - |
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- | 0.1178 | 1550 | 0.0046 | - |
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- | 0.1216 | 1600 | 0.0053 | - |
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- | 0.1254 | 1650 | 0.0464 | - |
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- | 0.1292 | 1700 | 0.0043 | - |
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- | 0.1330 | 1750 | 0.0403 | - |
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- | 0.1368 | 1800 | 0.0609 | - |
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- | 0.1406 | 1850 | 0.0093 | - |
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- | 0.1444 | 1900 | 0.0027 | - |
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- | 0.1482 | 1950 | 0.0041 | - |
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- | 0.1520 | 2000 | 0.0028 | - |
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- | 0.1558 | 2050 | 0.0072 | - |
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- | 0.1596 | 2100 | 0.0033 | - |
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- | 0.1634 | 2150 | 0.0029 | - |
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- | 0.1672 | 2200 | 0.0036 | - |
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- | 0.1710 | 2250 | 0.0019 | - |
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- | 0.1748 | 2300 | 0.0026 | - |
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- | 0.1786 | 2350 | 0.0544 | - |
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- | 0.1824 | 2400 | 0.0024 | - |
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- | 0.1862 | 2450 | 0.0028 | - |
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- | 0.1900 | 2500 | 0.0025 | - |
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- | 0.1938 | 2550 | 0.0018 | - |
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- | 0.1976 | 2600 | 0.0021 | - |
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- | 0.2014 | 2650 | 0.0023 | - |
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- | 0.2052 | 2700 | 0.0021 | - |
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- | 0.2090 | 2750 | 0.0026 | - |
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- | 0.2127 | 2800 | 0.0016 | - |
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- | 0.2165 | 2850 | 0.0023 | - |
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- | 0.2203 | 2900 | 0.0032 | - |
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- | 0.2241 | 2950 | 0.0019 | - |
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- | 0.2279 | 3000 | 0.0027 | - |
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- | 0.2317 | 3050 | 0.0035 | - |
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- | 0.2355 | 3100 | 0.0022 | - |
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- | 0.2393 | 3150 | 0.0019 | - |
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- | 0.2431 | 3200 | 0.0017 | - |
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- | 0.2469 | 3250 | 0.0016 | - |
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- | 0.2507 | 3300 | 0.0016 | - |
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- | 0.2545 | 3350 | 0.0017 | - |
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- | 0.2583 | 3400 | 0.0029 | - |
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- | 0.2621 | 3450 | 0.0017 | - |
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- | 0.2659 | 3500 | 0.0016 | - |
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- | 0.2697 | 3550 | 0.0019 | - |
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- | 0.2735 | 3600 | 0.0093 | - |
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- | 0.2773 | 3650 | 0.0023 | - |
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- | 0.2811 | 3700 | 0.0012 | - |
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- | 0.2849 | 3750 | 0.0016 | - |
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- | 0.2887 | 3800 | 0.0016 | - |
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- | 0.2925 | 3850 | 0.0021 | - |
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- | 0.2963 | 3900 | 0.0016 | - |
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- | 0.3001 | 3950 | 0.0017 | - |
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- | 0.3039 | 4000 | 0.0013 | - |
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- | 0.3077 | 4050 | 0.0017 | - |
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- | 0.3115 | 4100 | 0.0011 | - |
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- | 0.3153 | 4150 | 0.002 | - |
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- | 0.3191 | 4200 | 0.0015 | - |
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- | 0.3229 | 4250 | 0.001 | - |
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- | 0.3267 | 4300 | 0.0017 | - |
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- | 0.3305 | 4350 | 0.0011 | - |
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- | 0.3343 | 4400 | 0.0061 | - |
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- | 0.3381 | 4450 | 0.0057 | - |
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- | 0.3419 | 4500 | 0.0465 | - |
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- | 0.3457 | 4550 | 0.0016 | - |
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- | 0.3495 | 4600 | 0.0014 | - |
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- | 0.3533 | 4650 | 0.0013 | - |
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- | 0.3571 | 4700 | 0.0014 | - |
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- | 0.3609 | 4750 | 0.0018 | - |
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- | 0.3647 | 4800 | 0.0014 | - |
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- | 0.3685 | 4850 | 0.0013 | - |
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- | 0.3723 | 4900 | 0.0009 | - |
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- | 0.3761 | 4950 | 0.0008 | - |
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- | 0.3799 | 5000 | 0.0011 | - |
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- | 0.3837 | 5050 | 0.002 | - |
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- | 0.3875 | 5100 | 0.0014 | - |
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- | 0.3913 | 5150 | 0.001 | - |
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- | 0.3951 | 5200 | 0.0012 | - |
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- | 0.3989 | 5250 | 0.0017 | - |
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- | 0.4027 | 5300 | 0.0011 | - |
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- | 0.4065 | 5350 | 0.0012 | - |
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- | 0.4103 | 5400 | 0.0009 | - |
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- | 0.4141 | 5450 | 0.0015 | - |
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- | 0.4179 | 5500 | 0.0009 | - |
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- | 0.4217 | 5550 | 0.0012 | - |
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- | 0.4255 | 5600 | 0.0013 | - |
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- | 0.4293 | 5650 | 0.0465 | - |
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- | 0.4331 | 5700 | 0.0011 | - |
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- | 0.4369 | 5750 | 0.0008 | - |
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- | 0.4407 | 5800 | 0.0012 | - |
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- | 0.4445 | 5850 | 0.0008 | - |
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- | 0.4483 | 5900 | 0.0013 | - |
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- | 0.4521 | 5950 | 0.0011 | - |
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- | 0.4559 | 6000 | 0.0229 | - |
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- | 0.4597 | 6050 | 0.0012 | - |
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- | 0.4635 | 6100 | 0.0009 | - |
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- | 0.4673 | 6150 | 0.0011 | - |
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- | 0.4711 | 6200 | 0.0011 | - |
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- | 0.4749 | 6250 | 0.001 | - |
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- | 0.4787 | 6300 | 0.0008 | - |
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- | 0.4825 | 6350 | 0.0011 | - |
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- | 0.4863 | 6400 | 0.0012 | - |
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- | 0.4901 | 6450 | 0.0008 | - |
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- | 0.4939 | 6500 | 0.0014 | - |
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- | 0.4977 | 6550 | 0.001 | - |
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- | 0.5015 | 6600 | 0.0014 | - |
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- | 0.5053 | 6650 | 0.001 | - |
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- | 0.5091 | 6700 | 0.0008 | - |
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- | 0.5129 | 6750 | 0.0013 | - |
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- | 0.5167 | 6800 | 0.0012 | - |
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- | 0.5205 | 6850 | 0.0009 | - |
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- | 0.5243 | 6900 | 0.0008 | - |
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- | 0.5281 | 6950 | 0.001 | - |
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- | 0.5319 | 7000 | 0.0012 | - |
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- | 0.5357 | 7050 | 0.0009 | - |
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- | 0.5395 | 7100 | 0.0007 | - |
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- | 0.5433 | 7150 | 0.0008 | - |
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- | 0.5471 | 7200 | 0.001 | - |
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- | 0.5509 | 7250 | 0.0006 | - |
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- | 0.5547 | 7300 | 0.0007 | - |
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- | 0.5585 | 7350 | 0.0012 | - |
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- | 0.5623 | 7400 | 0.0159 | - |
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- | 0.5661 | 7450 | 0.0008 | - |
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- | 0.5699 | 7500 | 0.0012 | - |
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- | 0.5737 | 7550 | 0.0011 | - |
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- | 0.5775 | 7600 | 0.0008 | - |
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- | 0.5813 | 7650 | 0.0009 | - |
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- | 0.5851 | 7700 | 0.0005 | - |
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- | 0.5889 | 7750 | 0.0017 | - |
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- | 0.5927 | 7800 | 0.0009 | - |
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- | 0.5965 | 7850 | 0.0007 | - |
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- | 0.6003 | 7900 | 0.0065 | - |
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- | 0.6041 | 7950 | 0.0007 | - |
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- | 0.6079 | 8000 | 0.0041 | - |
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- | 0.6117 | 8050 | 0.0009 | - |
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- | 0.6155 | 8100 | 0.038 | - |
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- | 0.6193 | 8150 | 0.0005 | - |
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- | 0.6231 | 8200 | 0.0356 | - |
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- | 0.6269 | 8250 | 0.0007 | - |
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- | 0.6307 | 8300 | 0.0008 | - |
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- | 0.6345 | 8350 | 0.0009 | - |
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- | 0.6382 | 8400 | 0.001 | - |
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- | 0.6420 | 8450 | 0.0009 | - |
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- | 0.6458 | 8500 | 0.0008 | - |
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- | 0.6496 | 8550 | 0.0009 | - |
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- | 0.6534 | 8600 | 0.0009 | - |
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- | 0.6572 | 8650 | 0.0008 | - |
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- | 0.6610 | 8700 | 0.0006 | - |
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- | 0.6648 | 8750 | 0.0009 | - |
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- | 0.6686 | 8800 | 0.0006 | - |
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- | 0.6724 | 8850 | 0.0008 | - |
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- | 0.6762 | 8900 | 0.0008 | - |
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- | 0.6800 | 8950 | 0.0245 | - |
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- | 0.6838 | 9000 | 0.0007 | - |
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- | 0.6876 | 9050 | 0.0008 | - |
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- | 0.6914 | 9100 | 0.0007 | - |
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- | 0.6952 | 9150 | 0.0006 | - |
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- | 0.6990 | 9200 | 0.0009 | - |
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- | 0.7028 | 9250 | 0.0011 | - |
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- | 0.7066 | 9300 | 0.0009 | - |
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- | 0.7104 | 9350 | 0.0008 | - |
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- | 0.7142 | 9400 | 0.0008 | - |
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- | 0.7180 | 9450 | 0.0007 | - |
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- | 0.7218 | 9500 | 0.0006 | - |
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- | 0.7256 | 9550 | 0.0233 | - |
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- | 0.7294 | 9600 | 0.0008 | - |
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- | 0.7332 | 9650 | 0.0173 | - |
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- | 0.7370 | 9700 | 0.0006 | - |
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- | 0.7408 | 9750 | 0.0007 | - |
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- | 0.7446 | 9800 | 0.0007 | - |
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- | 0.7484 | 9850 | 0.001 | - |
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- | 0.7522 | 9900 | 0.0007 | - |
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- | 0.7560 | 9950 | 0.0006 | - |
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- | 0.7598 | 10000 | 0.0006 | - |
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- | 0.7636 | 10050 | 0.0008 | - |
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- | 0.7674 | 10100 | 0.0005 | - |
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- | 0.7712 | 10150 | 0.0007 | - |
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- | 0.7750 | 10200 | 0.0007 | - |
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- | 0.7788 | 10250 | 0.0009 | - |
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- | 0.7826 | 10300 | 0.0008 | - |
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- | 0.7864 | 10350 | 0.0007 | - |
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- | 0.7902 | 10400 | 0.0009 | - |
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- | 0.7940 | 10450 | 0.0007 | - |
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- | 0.7978 | 10500 | 0.0007 | - |
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- | 0.8016 | 10550 | 0.0008 | - |
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- | 0.8054 | 10600 | 0.0007 | - |
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- | 0.8092 | 10650 | 0.0007 | - |
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- | 0.8130 | 10700 | 0.0007 | - |
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- | 0.8168 | 10750 | 0.0007 | - |
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- | 0.8206 | 10800 | 0.0005 | - |
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- | 0.8244 | 10850 | 0.0007 | - |
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- | 0.8282 | 10900 | 0.0005 | - |
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- | 0.8320 | 10950 | 0.0005 | - |
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- | 0.8358 | 11000 | 0.0006 | - |
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- | 0.8396 | 11050 | 0.0008 | - |
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- | 0.8434 | 11100 | 0.0008 | - |
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- | 0.8472 | 11150 | 0.0137 | - |
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- | 0.8510 | 11200 | 0.0008 | - |
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- | 0.8548 | 11250 | 0.012 | - |
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- | 0.8586 | 11300 | 0.0006 | - |
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- | 0.8624 | 11350 | 0.0007 | - |
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- | 0.8662 | 11400 | 0.0007 | - |
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- | 0.8700 | 11450 | 0.0009 | - |
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- | 0.8738 | 11500 | 0.0007 | - |
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- | 0.8776 | 11550 | 0.0008 | - |
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- | 0.8814 | 11600 | 0.0005 | - |
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- | 0.8852 | 11650 | 0.0008 | - |
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- | 0.8890 | 11700 | 0.0008 | - |
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- | 0.8928 | 11750 | 0.0007 | - |
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- | 0.8966 | 11800 | 0.0006 | - |
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- | 0.9004 | 11850 | 0.0006 | - |
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- | 0.9042 | 11900 | 0.0006 | - |
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- | 0.9080 | 11950 | 0.0007 | - |
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- | 0.9118 | 12000 | 0.0005 | - |
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- | 0.9156 | 12050 | 0.0007 | - |
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- | 0.9194 | 12100 | 0.0006 | - |
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- | 0.9232 | 12150 | 0.0008 | - |
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- | 0.9270 | 12200 | 0.0006 | - |
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- | 0.9308 | 12250 | 0.0005 | - |
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- | 0.9346 | 12300 | 0.0167 | - |
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- | 0.9384 | 12350 | 0.0008 | - |
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- | 0.9422 | 12400 | 0.0005 | - |
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- | 0.9460 | 12450 | 0.0233 | - |
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- | 0.9498 | 12500 | 0.001 | - |
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- | 0.9536 | 12550 | 0.0006 | - |
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- | 0.9574 | 12600 | 0.0007 | - |
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- | 0.9612 | 12650 | 0.0007 | - |
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- | 0.9650 | 12700 | 0.0006 | - |
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- | 0.9688 | 12750 | 0.0008 | - |
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- | 0.9726 | 12800 | 0.0006 | - |
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- | 0.9764 | 12850 | 0.0177 | - |
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- | 0.9802 | 12900 | 0.0008 | - |
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- | 0.9840 | 12950 | 0.0007 | - |
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- | 0.9878 | 13000 | 0.0131 | - |
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- | 0.9916 | 13050 | 0.0007 | - |
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- | 0.9954 | 13100 | 0.0006 | - |
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- | 0.9992 | 13150 | 0.0004 | - |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
413
 
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  ### Framework Versions
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  - Python: 3.10.13
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  - SetFit: 1.0.3
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- - Sentence Transformers: 2.6.1
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  - spaCy: 3.7.2
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  - Transformers: 4.39.3
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  - PyTorch: 2.1.2
 
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  metrics:
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  - accuracy
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  widget:
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+ - text: плюсов -:Еще из плюсов - при заказе банкета есть специальное предложение по
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+ алкоголю ( можно приобрети вино , шампанское и водку по ценам производителя )
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+ .
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+ - text: телятины:Заказала я салат , большую порцию , как ни странно его принесли в
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+ большом количестве , из горячего заказала стейк из телятины , мясо было мягким
18
+ и сочным , и конечно же мое самое любимое это десерт , заказала тирамису , и правильно
19
+ сделала , очень вкусный десерт .
20
+ - text: бекона:Салат цезарь вся тарелка это листья салата , немного бекона по кругу
21
+ и все это в соусе , сверху сыр ( цезарь готовится с курицей ) .
22
+ - text: ресторан:По моей рекомендации этот ресторан посетили несколько пар моих друзей
23
+ и также остались довольны .
24
+ - text: блюда:Для меня же минус был в том , что сами блюда слишком специфические .
25
  pipeline_tag: text-classification
26
  inference: false
27
  ---
 
47
  - **Model Type:** SetFit
48
  - **Sentence Transformer body:** [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2)
49
  - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
50
+ - **spaCy Model:** ru_core_news_lg
51
  - **SetFitABSA Aspect Model:** [isolation-forest/setfit-absa-aspect](https://huggingface.co/isolation-forest/setfit-absa-aspect)
52
  - **SetFitABSA Polarity Model:** [isolation-forest/setfit-absa-polarity](https://huggingface.co/isolation-forest/setfit-absa-polarity)
53
  - **Maximum Sequence Length:** 2048 tokens
 
63
  - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
64
 
65
  ### Model Labels
66
+ | Label | Examples |
67
+ |:----------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
68
+ | aspect | <ul><li>'порции:И порции " достойные " .'</li><li>'официантка:Потом официантка как будто пропала , было не дозваться , чтобы что - то дозаказать , очень долго приходилось ждать , в итоге посчитали неправильно , в счет внесли на 2 пункта больше , чем мы заказывали .'</li><li>'Обслуживание:Обслуживание не впечатлило .'</li></ul> |
69
+ | no aspect | <ul><li>'итоге:Потом официантка как будто пропала , было не дозваться , чтобы что - то дозаказать , очень долго приходилось ждать , в итоге посчитали неправильно , в счет внесли на 2 пункта больше , чем мы заказывали .'</li><li>'счет:Потом официантка как будто пропала , было не дозваться , чтобы что - то дозаказать , очень долго приходилось ждать , в итоге посчитали неправильно , в счет внесли на 2 пункта больше , чем мы заказывали .'</li><li>'пункта:Потом официантка как будто пропала , было не дозваться , чтобы что - то дозаказать , очень долго приходилось ждать , в итоге посчитали неправильно , в счет внесли на 2 пункта больше , чем мы заказывали .'</li></ul> |
70
 
71
  ## Uses
72
 
 
121
  ### Training Set Metrics
122
  | Training set | Min | Median | Max |
123
  |:-------------|:----|:--------|:----|
124
+ | Word count | 2 | 31.9677 | 88 |
125
 
126
  | Label | Training Sample Count |
127
  |:----------|:----------------------|
128
+ | no aspect | 797 |
129
  | aspect | 256 |
130
 
131
  ### Training Hyperparameters
 
148
  ### Training Results
149
  | Epoch | Step | Training Loss | Validation Loss |
150
  |:------:|:-----:|:-------------:|:---------------:|
151
+ | 0.0000 | 1 | 0.25 | - |
152
+ | 0.0011 | 50 | 0.1976 | - |
153
+ | 0.0023 | 100 | 0.2289 | - |
154
+ | 0.0034 | 150 | 0.2826 | - |
155
+ | 0.0046 | 200 | 0.2361 | - |
156
+ | 0.0057 | 250 | 0.2766 | - |
157
+ | 0.0068 | 300 | 0.2723 | - |
158
+ | 0.0080 | 350 | 0.2402 | - |
159
+ | 0.0091 | 400 | 0.2678 | - |
160
+ | 0.0103 | 450 | 0.2511 | - |
161
+ | 0.0114 | 500 | 0.21 | - |
162
+ | 0.0125 | 550 | 0.2503 | - |
163
+ | 0.0137 | 600 | 0.2614 | - |
164
+ | 0.0148 | 650 | 0.218 | - |
165
+ | 0.0160 | 700 | 0.2482 | - |
166
+ | 0.0171 | 750 | 0.2091 | - |
167
+ | 0.0182 | 800 | 0.2477 | - |
168
+ | 0.0194 | 850 | 0.2531 | - |
169
+ | 0.0205 | 900 | 0.1878 | - |
170
+ | 0.0217 | 950 | 0.2416 | - |
171
+ | 0.0228 | 1000 | 0.2245 | - |
172
+ | 0.0239 | 1050 | 0.2367 | - |
173
+ | 0.0251 | 1100 | 0.2376 | - |
174
+ | 0.0262 | 1150 | 0.2271 | - |
175
+ | 0.0274 | 1200 | 0.228 | - |
176
+ | 0.0285 | 1250 | 0.2362 | - |
177
+ | 0.0296 | 1300 | 0.2308 | - |
178
+ | 0.0308 | 1350 | 0.2326 | - |
179
+ | 0.0319 | 1400 | 0.2535 | - |
180
+ | 0.0331 | 1450 | 0.177 | - |
181
+ | 0.0342 | 1500 | 0.2595 | - |
182
+ | 0.0353 | 1550 | 0.2289 | - |
183
+ | 0.0365 | 1600 | 0.2378 | - |
184
+ | 0.0376 | 1650 | 0.2111 | - |
185
+ | 0.0388 | 1700 | 0.2556 | - |
186
+ | 0.0399 | 1750 | 0.2054 | - |
187
+ | 0.0410 | 1800 | 0.1949 | - |
188
+ | 0.0422 | 1850 | 0.2065 | - |
189
+ | 0.0433 | 1900 | 0.1907 | - |
190
+ | 0.0445 | 1950 | 0.2325 | - |
191
+ | 0.0456 | 2000 | 0.2313 | - |
192
+ | 0.0467 | 2050 | 0.1713 | - |
193
+ | 0.0479 | 2100 | 0.1786 | - |
194
+ | 0.0490 | 2150 | 0.2258 | - |
195
+ | 0.0502 | 2200 | 0.1102 | - |
196
+ | 0.0513 | 2250 | 0.1714 | - |
197
+ | 0.0524 | 2300 | 0.2325 | - |
198
+ | 0.0536 | 2350 | 0.2287 | - |
199
+ | 0.0547 | 2400 | 0.2901 | - |
200
+ | 0.0559 | 2450 | 0.1763 | - |
201
+ | 0.0570 | 2500 | 0.223 | - |
202
+ | 0.0581 | 2550 | 0.0784 | - |
203
+ | 0.0593 | 2600 | 0.2069 | - |
204
+ | 0.0604 | 2650 | 0.1353 | - |
205
+ | 0.0616 | 2700 | 0.1729 | - |
206
+ | 0.0627 | 2750 | 0.1753 | - |
207
+ | 0.0638 | 2800 | 0.2243 | - |
208
+ | 0.0650 | 2850 | 0.1151 | - |
209
+ | 0.0661 | 2900 | 0.2547 | - |
210
+ | 0.0673 | 2950 | 0.1414 | - |
211
+ | 0.0684 | 3000 | 0.1771 | - |
212
+ | 0.0695 | 3050 | 0.1275 | - |
213
+ | 0.0707 | 3100 | 0.0541 | - |
214
+ | 0.0718 | 3150 | 0.0962 | - |
215
+ | 0.0730 | 3200 | 0.1953 | - |
216
+ | 0.0741 | 3250 | 0.0787 | - |
217
+ | 0.0752 | 3300 | 0.0766 | - |
218
+ | 0.0764 | 3350 | 0.1537 | - |
219
+ | 0.0775 | 3400 | 0.0957 | - |
220
+ | 0.0787 | 3450 | 0.0975 | - |
221
+ | 0.0798 | 3500 | 0.0359 | - |
222
+ | 0.0809 | 3550 | 0.0402 | - |
223
+ | 0.0821 | 3600 | 0.0377 | - |
224
+ | 0.0832 | 3650 | 0.0486 | - |
225
+ | 0.0844 | 3700 | 0.1206 | - |
226
+ | 0.0855 | 3750 | 0.0504 | - |
227
+ | 0.0866 | 3800 | 0.1072 | - |
228
+ | 0.0878 | 3850 | 0.0838 | - |
229
+ | 0.0889 | 3900 | 0.1222 | - |
230
+ | 0.0901 | 3950 | 0.0463 | - |
231
+ | 0.0912 | 4000 | 0.0781 | - |
232
+ | 0.0923 | 4050 | 0.031 | - |
233
+ | 0.0935 | 4100 | 0.1063 | - |
234
+ | 0.0946 | 4150 | 0.0643 | - |
235
+ | 0.0958 | 4200 | 0.0624 | - |
236
+ | 0.0969 | 4250 | 0.0283 | - |
237
+ | 0.0980 | 4300 | 0.0527 | - |
238
+ | 0.0992 | 4350 | 0.0153 | - |
239
+ | 0.1003 | 4400 | 0.0765 | - |
240
+ | 0.1015 | 4450 | 0.0245 | - |
241
+ | 0.1026 | 4500 | 0.0494 | - |
242
+ | 0.1037 | 4550 | 0.0218 | - |
243
+ | 0.1049 | 4600 | 0.0086 | - |
244
+ | 0.1060 | 4650 | 0.0245 | - |
245
+ | 0.1072 | 4700 | 0.0047 | - |
246
+ | 0.1083 | 4750 | 0.0284 | - |
247
+ | 0.1094 | 4800 | 0.0045 | - |
248
+ | 0.1106 | 4850 | 0.0683 | - |
249
+ | 0.1117 | 4900 | 0.0234 | - |
250
+ | 0.1129 | 4950 | 0.0584 | - |
251
+ | 0.1140 | 5000 | 0.1212 | - |
252
+ | 0.1151 | 5050 | 0.0052 | - |
253
+ | 0.1163 | 5100 | 0.065 | - |
254
+ | 0.1174 | 5150 | 0.003 | - |
255
+ | 0.1186 | 5200 | 0.0937 | - |
256
+ | 0.1197 | 5250 | 0.0038 | - |
257
+ | 0.1208 | 5300 | 0.0061 | - |
258
+ | 0.1220 | 5350 | 0.0038 | - |
259
+ | 0.1231 | 5400 | 0.0674 | - |
260
+ | 0.1243 | 5450 | 0.0039 | - |
261
+ | 0.1254 | 5500 | 0.0088 | - |
262
+ | 0.1265 | 5550 | 0.0028 | - |
263
+ | 0.1277 | 5600 | 0.0031 | - |
264
+ | 0.1288 | 5650 | 0.0035 | - |
265
+ | 0.1300 | 5700 | 0.0545 | - |
266
+ | 0.1311 | 5750 | 0.0021 | - |
267
+ | 0.1322 | 5800 | 0.0056 | - |
268
+ | 0.1334 | 5850 | 0.0019 | - |
269
+ | 0.1345 | 5900 | 0.0023 | - |
270
+ | 0.1356 | 5950 | 0.0595 | - |
271
+ | 0.1368 | 6000 | 0.0019 | - |
272
+ | 0.1379 | 6050 | 0.0031 | - |
273
+ | 0.1391 | 6100 | 0.0025 | - |
274
+ | 0.1402 | 6150 | 0.0026 | - |
275
+ | 0.1413 | 6200 | 0.0032 | - |
276
+ | 0.1425 | 6250 | 0.0019 | - |
277
+ | 0.1436 | 6300 | 0.0761 | - |
278
+ | 0.1448 | 6350 | 0.0446 | - |
279
+ | 0.1459 | 6400 | 0.002 | - |
280
+ | 0.1470 | 6450 | 0.008 | - |
281
+ | 0.1482 | 6500 | 0.0044 | - |
282
+ | 0.1493 | 6550 | 0.0024 | - |
283
+ | 0.1505 | 6600 | 0.0026 | - |
284
+ | 0.1516 | 6650 | 0.0477 | - |
285
+ | 0.1527 | 6700 | 0.0023 | - |
286
+ | 0.1539 | 6750 | 0.0024 | - |
287
+ | 0.1550 | 6800 | 0.0016 | - |
288
+ | 0.1562 | 6850 | 0.0023 | - |
289
+ | 0.1573 | 6900 | 0.0017 | - |
290
+ | 0.1584 | 6950 | 0.0026 | - |
291
+ | 0.1596 | 7000 | 0.0602 | - |
292
+ | 0.1607 | 7050 | 0.002 | - |
293
+ | 0.1619 | 7100 | 0.0014 | - |
294
+ | 0.1630 | 7150 | 0.0019 | - |
295
+ | 0.1641 | 7200 | 0.0019 | - |
296
+ | 0.1653 | 7250 | 0.0021 | - |
297
+ | 0.1664 | 7300 | 0.0563 | - |
298
+ | 0.1676 | 7350 | 0.0017 | - |
299
+ | 0.1687 | 7400 | 0.0019 | - |
300
+ | 0.1698 | 7450 | 0.0017 | - |
301
+ | 0.1710 | 7500 | 0.0014 | - |
302
+ | 0.1721 | 7550 | 0.002 | - |
303
+ | 0.1733 | 7600 | 0.0028 | - |
304
+ | 0.1744 | 7650 | 0.002 | - |
305
+ | 0.1755 | 7700 | 0.0021 | - |
306
+ | 0.1767 | 7750 | 0.002 | - |
307
+ | 0.1778 | 7800 | 0.0017 | - |
308
+ | 0.1790 | 7850 | 0.0579 | - |
309
+ | 0.1801 | 7900 | 0.0089 | - |
310
+ | 0.1812 | 7950 | 0.0016 | - |
311
+ | 0.1824 | 8000 | 0.104 | - |
312
+ | 0.1835 | 8050 | 0.0241 | - |
313
+ | 0.1847 | 8100 | 0.0015 | - |
314
+ | 0.1858 | 8150 | 0.0039 | - |
315
+ | 0.1869 | 8200 | 0.0018 | - |
316
+ | 0.1881 | 8250 | 0.0018 | - |
317
+ | 0.1892 | 8300 | 0.0012 | - |
318
+ | 0.1904 | 8350 | 0.0015 | - |
319
+ | 0.1915 | 8400 | 0.0016 | - |
320
+ | 0.1926 | 8450 | 0.0017 | - |
321
+ | 0.1938 | 8500 | 0.0647 | - |
322
+ | 0.1949 | 8550 | 0.0013 | - |
323
+ | 0.1961 | 8600 | 0.0014 | - |
324
+ | 0.1972 | 8650 | 0.1705 | - |
325
+ | 0.1983 | 8700 | 0.0036 | - |
326
+ | 0.1995 | 8750 | 0.0014 | - |
327
+ | 0.2006 | 8800 | 0.0021 | - |
328
+ | 0.2018 | 8850 | 0.0019 | - |
329
+ | 0.2029 | 8900 | 0.0018 | - |
330
+ | 0.2040 | 8950 | 0.0018 | - |
331
+ | 0.2052 | 9000 | 0.001 | - |
332
+ | 0.2063 | 9050 | 0.0012 | - |
333
+ | 0.2075 | 9100 | 0.0013 | - |
334
+ | 0.2086 | 9150 | 0.0014 | - |
335
+ | 0.2097 | 9200 | 0.0609 | - |
336
+ | 0.2109 | 9250 | 0.0026 | - |
337
+ | 0.2120 | 9300 | 0.0012 | - |
338
+ | 0.2132 | 9350 | 0.0023 | - |
339
+ | 0.2143 | 9400 | 0.0043 | - |
340
+ | 0.2154 | 9450 | 0.0511 | - |
341
+ | 0.2166 | 9500 | 0.0012 | - |
342
+ | 0.2177 | 9550 | 0.002 | - |
343
+ | 0.2189 | 9600 | 0.0016 | - |
344
+ | 0.2200 | 9650 | 0.0124 | - |
345
+ | 0.2211 | 9700 | 0.0046 | - |
346
+ | 0.2223 | 9750 | 0.0012 | - |
347
+ | 0.2234 | 9800 | 0.0014 | - |
348
+ | 0.2246 | 9850 | 0.0016 | - |
349
+ | 0.2257 | 9900 | 0.0596 | - |
350
+ | 0.2268 | 9950 | 0.0013 | - |
351
+ | 0.2280 | 10000 | 0.0021 | - |
352
+ | 0.2291 | 10050 | 0.0012 | - |
353
+ | 0.2303 | 10100 | 0.057 | - |
354
+ | 0.2314 | 10150 | 0.0028 | - |
355
+ | 0.2325 | 10200 | 0.0014 | - |
356
+ | 0.2337 | 10250 | 0.0014 | - |
357
+ | 0.2348 | 10300 | 0.0019 | - |
358
+ | 0.2360 | 10350 | 0.0014 | - |
359
+ | 0.2371 | 10400 | 0.0015 | - |
360
+ | 0.2382 | 10450 | 0.0569 | - |
361
+ | 0.2394 | 10500 | 0.0012 | - |
362
+ | 0.2405 | 10550 | 0.0023 | - |
363
+ | 0.2417 | 10600 | 0.0013 | - |
364
+ | 0.2428 | 10650 | 0.0011 | - |
365
+ | 0.2439 | 10700 | 0.0191 | - |
366
+ | 0.2451 | 10750 | 0.0015 | - |
367
+ | 0.2462 | 10800 | 0.0022 | - |
368
+ | 0.2474 | 10850 | 0.0547 | - |
369
+ | 0.2485 | 10900 | 0.003 | - |
370
+ | 0.2496 | 10950 | 0.0013 | - |
371
+ | 0.2508 | 11000 | 0.0018 | - |
372
+ | 0.2519 | 11050 | 0.0016 | - |
373
+ | 0.2531 | 11100 | 0.0013 | - |
374
+ | 0.2542 | 11150 | 0.0019 | - |
375
+ | 0.2553 | 11200 | 0.0011 | - |
376
+ | 0.2565 | 11250 | 0.0555 | - |
377
+ | 0.2576 | 11300 | 0.0012 | - |
378
+ | 0.2588 | 11350 | 0.0016 | - |
379
+ | 0.2599 | 11400 | 0.004 | - |
380
+ | 0.2610 | 11450 | 0.0014 | - |
381
+ | 0.2622 | 11500 | 0.0016 | - |
382
+ | 0.2633 | 11550 | 0.0037 | - |
383
+ | 0.2645 | 11600 | 0.0014 | - |
384
+ | 0.2656 | 11650 | 0.0252 | - |
385
+ | 0.2667 | 11700 | 0.0011 | - |
386
+ | 0.2679 | 11750 | 0.0013 | - |
387
+ | 0.2690 | 11800 | 0.0552 | - |
388
+ | 0.2702 | 11850 | 0.0019 | - |
389
+ | 0.2713 | 11900 | 0.0009 | - |
390
+ | 0.2724 | 11950 | 0.0015 | - |
391
+ | 0.2736 | 12000 | 0.0362 | - |
392
+ | 0.2747 | 12050 | 0.001 | - |
393
+ | 0.2759 | 12100 | 0.0022 | - |
394
+ | 0.2770 | 12150 | 0.0013 | - |
395
+ | 0.2781 | 12200 | 0.0013 | - |
396
+ | 0.2793 | 12250 | 0.001 | - |
397
+ | 0.2804 | 12300 | 0.0027 | - |
398
+ | 0.2816 | 12350 | 0.0013 | - |
399
+ | 0.2827 | 12400 | 0.0014 | - |
400
+ | 0.2838 | 12450 | 0.001 | - |
401
+ | 0.2850 | 12500 | 0.0014 | - |
402
+ | 0.2861 | 12550 | 0.0014 | - |
403
+ | 0.2873 | 12600 | 0.0407 | - |
404
+ | 0.2884 | 12650 | 0.0009 | - |
405
+ | 0.2895 | 12700 | 0.0014 | - |
406
+ | 0.2907 | 12750 | 0.001 | - |
407
+ | 0.2918 | 12800 | 0.0011 | - |
408
+ | 0.2930 | 12850 | 0.0012 | - |
409
+ | 0.2941 | 12900 | 0.0011 | - |
410
+ | 0.2952 | 12950 | 0.0016 | - |
411
+ | 0.2964 | 13000 | 0.0012 | - |
412
+ | 0.2975 | 13050 | 0.001 | - |
413
+ | 0.2987 | 13100 | 0.0026 | - |
414
+ | 0.2998 | 13150 | 0.0015 | - |
415
+ | 0.3009 | 13200 | 0.0022 | - |
416
+ | 0.3021 | 13250 | 0.0007 | - |
417
+ | 0.3032 | 13300 | 0.001 | - |
418
+ | 0.3044 | 13350 | 0.0012 | - |
419
+ | 0.3055 | 13400 | 0.0019 | - |
420
+ | 0.3066 | 13450 | 0.0016 | - |
421
+ | 0.3078 | 13500 | 0.0938 | - |
422
+ | 0.3089 | 13550 | 0.0009 | - |
423
+ | 0.3101 | 13600 | 0.0016 | - |
424
+ | 0.3112 | 13650 | 0.0014 | - |
425
+ | 0.3123 | 13700 | 0.032 | - |
426
+ | 0.3135 | 13750 | 0.0013 | - |
427
+ | 0.3146 | 13800 | 0.0219 | - |
428
+ | 0.3158 | 13850 | 0.0012 | - |
429
+ | 0.3169 | 13900 | 0.0012 | - |
430
+ | 0.3180 | 13950 | 0.0214 | - |
431
+ | 0.3192 | 14000 | 0.001 | - |
432
+ | 0.3203 | 14050 | 0.0033 | - |
433
+ | 0.3215 | 14100 | 0.0009 | - |
434
+ | 0.3226 | 14150 | 0.001 | - |
435
+ | 0.3237 | 14200 | 0.001 | - |
436
+ | 0.3249 | 14250 | 0.0014 | - |
437
+ | 0.3260 | 14300 | 0.0075 | - |
438
+ | 0.3272 | 14350 | 0.0015 | - |
439
+ | 0.3283 | 14400 | 0.0018 | - |
440
+ | 0.3294 | 14450 | 0.0011 | - |
441
+ | 0.3306 | 14500 | 0.0008 | - |
442
+ | 0.3317 | 14550 | 0.0381 | - |
443
+ | 0.3329 | 14600 | 0.0007 | - |
444
+ | 0.3340 | 14650 | 0.0009 | - |
445
+ | 0.3351 | 14700 | 0.001 | - |
446
+ | 0.3363 | 14750 | 0.0011 | - |
447
+ | 0.3374 | 14800 | 0.0304 | - |
448
+ | 0.3386 | 14850 | 0.0008 | - |
449
+ | 0.3397 | 14900 | 0.0007 | - |
450
+ | 0.3408 | 14950 | 0.0013 | - |
451
+ | 0.3420 | 15000 | 0.0135 | - |
452
+ | 0.3431 | 15050 | 0.001 | - |
453
+ | 0.3443 | 15100 | 0.0007 | - |
454
+ | 0.3454 | 15150 | 0.0008 | - |
455
+ | 0.3465 | 15200 | 0.0018 | - |
456
+ | 0.3477 | 15250 | 0.0009 | - |
457
+ | 0.3488 | 15300 | 0.0013 | - |
458
+ | 0.3500 | 15350 | 0.0018 | - |
459
+ | 0.3511 | 15400 | 0.0014 | - |
460
+ | 0.3522 | 15450 | 0.0051 | - |
461
+ | 0.3534 | 15500 | 0.0009 | - |
462
+ | 0.3545 | 15550 | 0.0007 | - |
463
+ | 0.3557 | 15600 | 0.0006 | - |
464
+ | 0.3568 | 15650 | 0.001 | - |
465
+ | 0.3579 | 15700 | 0.001 | - |
466
+ | 0.3591 | 15750 | 0.0015 | - |
467
+ | 0.3602 | 15800 | 0.0006 | - |
468
+ | 0.3614 | 15850 | 0.0005 | - |
469
+ | 0.3625 | 15900 | 0.0009 | - |
470
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471
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472
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473
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474
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475
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476
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477
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478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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493
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494
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495
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496
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497
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498
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499
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500
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501
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502
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503
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504
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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552
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553
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554
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555
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556
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557
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558
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559
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560
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561
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562
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563
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564
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565
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566
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567
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568
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569
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570
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571
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572
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573
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574
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575
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576
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577
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578
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579
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580
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581
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582
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583
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584
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585
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586
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587
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588
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589
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590
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591
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592
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593
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594
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595
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596
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597
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598
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599
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600
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601
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602
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603
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604
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605
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606
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607
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608
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609
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610
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611
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612
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613
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614
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615
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616
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617
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618
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619
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620
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621
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622
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623
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624
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625
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626
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627
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628
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629
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630
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631
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632
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633
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634
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635
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636
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637
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638
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639
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640
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641
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642
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643
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644
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645
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646
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647
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648
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649
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650
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651
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652
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653
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654
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655
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656
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657
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658
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659
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660
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661
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662
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663
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664
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665
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666
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667
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668
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669
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670
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671
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672
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673
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674
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675
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676
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677
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678
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679
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680
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681
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682
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683
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684
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685
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686
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687
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688
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689
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690
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691
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692
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693
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694
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695
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696
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697
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698
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699
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700
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701
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702
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703
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704
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705
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706
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707
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708
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709
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710
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711
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712
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713
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714
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715
+ | 0.6429 | 28200 | 0.0004 | - |
716
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717
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718
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719
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720
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721
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722
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723
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724
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725
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726
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727
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728
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729
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730
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731
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732
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733
+ | 0.6634 | 29100 | 0.0003 | - |
734
+ | 0.6646 | 29150 | 0.0003 | - |
735
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736
+ | 0.6668 | 29250 | 0.0004 | - |
737
+ | 0.6680 | 29300 | 0.0002 | - |
738
+ | 0.6691 | 29350 | 0.0006 | - |
739
+ | 0.6703 | 29400 | 0.0006 | - |
740
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741
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742
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743
+ | 0.6748 | 29600 | 0.0004 | - |
744
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745
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746
+ | 0.6782 | 29750 | 0.0003 | - |
747
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748
+ | 0.6805 | 29850 | 0.0007 | - |
749
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750
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751
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752
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753
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754
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755
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756
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757
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758
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759
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760
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761
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762
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763
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764
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765
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766
+ | 0.7010 | 30750 | 0.0007 | - |
767
+ | 0.7022 | 30800 | 0.0003 | - |
768
+ | 0.7033 | 30850 | 0.0005 | - |
769
+ | 0.7045 | 30900 | 0.0003 | - |
770
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771
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772
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773
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774
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775
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776
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777
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778
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779
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780
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781
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782
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783
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784
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785
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786
+ | 0.7238 | 31750 | 0.0003 | - |
787
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788
+ | 0.7261 | 31850 | 0.0004 | - |
789
+ | 0.7273 | 31900 | 0.0006 | - |
790
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791
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792
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793
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794
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795
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796
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797
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798
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799
+ | 0.7387 | 32400 | 0.0004 | - |
800
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801
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802
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803
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804
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805
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806
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807
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808
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809
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810
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811
+ | 0.7523 | 33000 | 0.0002 | - |
812
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813
+ | 0.7546 | 33100 | 0.0004 | - |
814
+ | 0.7558 | 33150 | 0.0002 | - |
815
+ | 0.7569 | 33200 | 0.0006 | - |
816
+ | 0.7580 | 33250 | 0.0046 | - |
817
+ | 0.7592 | 33300 | 0.0005 | - |
818
+ | 0.7603 | 33350 | 0.0003 | - |
819
+ | 0.7615 | 33400 | 0.0125 | - |
820
+ | 0.7626 | 33450 | 0.0006 | - |
821
+ | 0.7637 | 33500 | 0.0063 | - |
822
+ | 0.7649 | 33550 | 0.0008 | - |
823
+ | 0.7660 | 33600 | 0.0004 | - |
824
+ | 0.7672 | 33650 | 0.0037 | - |
825
+ | 0.7683 | 33700 | 0.0005 | - |
826
+ | 0.7694 | 33750 | 0.0006 | - |
827
+ | 0.7706 | 33800 | 0.0006 | - |
828
+ | 0.7717 | 33850 | 0.012 | - |
829
+ | 0.7729 | 33900 | 0.0005 | - |
830
+ | 0.7740 | 33950 | 0.0005 | - |
831
+ | 0.7751 | 34000 | 0.0005 | - |
832
+ | 0.7763 | 34050 | 0.0003 | - |
833
+ | 0.7774 | 34100 | 0.0004 | - |
834
+ | 0.7786 | 34150 | 0.0003 | - |
835
+ | 0.7797 | 34200 | 0.0003 | - |
836
+ | 0.7808 | 34250 | 0.0088 | - |
837
+ | 0.7820 | 34300 | 0.0004 | - |
838
+ | 0.7831 | 34350 | 0.0002 | - |
839
+ | 0.7843 | 34400 | 0.0004 | - |
840
+ | 0.7854 | 34450 | 0.0082 | - |
841
+ | 0.7865 | 34500 | 0.0005 | - |
842
+ | 0.7877 | 34550 | 0.0005 | - |
843
+ | 0.7888 | 34600 | 0.0004 | - |
844
+ | 0.7900 | 34650 | 0.0003 | - |
845
+ | 0.7911 | 34700 | 0.0006 | - |
846
+ | 0.7922 | 34750 | 0.0006 | - |
847
+ | 0.7934 | 34800 | 0.0002 | - |
848
+ | 0.7945 | 34850 | 0.0003 | - |
849
+ | 0.7957 | 34900 | 0.0005 | - |
850
+ | 0.7968 | 34950 | 0.0003 | - |
851
+ | 0.7979 | 35000 | 0.0004 | - |
852
+ | 0.7991 | 35050 | 0.0003 | - |
853
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854
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855
+ | 0.8025 | 35200 | 0.0004 | - |
856
+ | 0.8036 | 35250 | 0.0004 | - |
857
+ | 0.8048 | 35300 | 0.0245 | - |
858
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859
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860
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861
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862
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863
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864
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865
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866
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867
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868
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869
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870
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871
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872
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874
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880
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886
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890
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891
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899
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948
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1009
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1030
  ### Framework Versions
1031
  - Python: 3.10.13
1032
  - SetFit: 1.0.3
1033
+ - Sentence Transformers: 2.7.0
1034
  - spaCy: 3.7.2
1035
  - Transformers: 4.39.3
1036
  - PyTorch: 2.1.2
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