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distilbert_finetune_own_data_model

Token Classification
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distilbert
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End of training

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  1. README.md +67 -37
  2. model.safetensors +1 -1
  3. runs/Mar04_09-08-39_561d3d140928/events.out.tfevents.1709543334.561d3d140928.219.0 +3 -0
  4. runs/Mar04_09-09-21_561d3d140928/events.out.tfevents.1709543375.561d3d140928.219.1 +3 -0
  5. runs/Mar04_09-10-00_561d3d140928/events.out.tfevents.1709543412.561d3d140928.219.2 +3 -0
  6. runs/Mar04_09-10-00_561d3d140928/events.out.tfevents.1709543441.561d3d140928.219.3 +3 -0
  7. training_args.bin +1 -1
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5806
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- - Precision: 0.8333
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- - Recall: 0.4167
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- - F1: 0.5556
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- - Accuracy: 0.8621
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  ## Model description
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@@ -43,48 +43,78 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 2e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 30
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.0 | 7 | 0.7508 | 0.0 | 0.0 | 0.0 | 0.7241 |
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- | No log | 2.0 | 14 | 0.6957 | 0.0 | 0.0 | 0.0 | 0.7241 |
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- | No log | 3.0 | 21 | 0.5574 | 0.0 | 0.0 | 0.0 | 0.7241 |
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- | No log | 4.0 | 28 | 0.4661 | 0.0 | 0.0 | 0.0 | 0.7414 |
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- | No log | 5.0 | 35 | 0.4346 | 0.2857 | 0.1667 | 0.2105 | 0.7931 |
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- | No log | 6.0 | 42 | 0.3960 | 0.5714 | 0.3333 | 0.4211 | 0.8448 |
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- | No log | 7.0 | 49 | 0.3890 | 0.5 | 0.3333 | 0.4 | 0.8621 |
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- | No log | 8.0 | 56 | 0.4204 | 0.5714 | 0.3333 | 0.4211 | 0.8448 |
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- | No log | 9.0 | 63 | 0.4701 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
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- | No log | 10.0 | 70 | 0.5037 | 0.5714 | 0.3333 | 0.4211 | 0.8448 |
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- | No log | 11.0 | 77 | 0.5123 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
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- | No log | 12.0 | 84 | 0.5306 | 0.5714 | 0.3333 | 0.4211 | 0.8448 |
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- | No log | 13.0 | 91 | 0.5420 | 0.5714 | 0.3333 | 0.4211 | 0.8448 |
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- | No log | 14.0 | 98 | 0.5358 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
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- | No log | 15.0 | 105 | 0.5314 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
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- | No log | 16.0 | 112 | 0.5379 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
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- | No log | 17.0 | 119 | 0.5487 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
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- | No log | 18.0 | 126 | 0.5572 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
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- | No log | 19.0 | 133 | 0.5629 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
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- | No log | 20.0 | 140 | 0.5665 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
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- | No log | 21.0 | 147 | 0.5691 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
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- | No log | 22.0 | 154 | 0.5714 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
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- | No log | 23.0 | 161 | 0.5733 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
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- | No log | 24.0 | 168 | 0.5746 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
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- | No log | 25.0 | 175 | 0.5759 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
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- | No log | 26.0 | 182 | 0.5768 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
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- | No log | 27.0 | 189 | 0.5786 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
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- | No log | 28.0 | 196 | 0.5798 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
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- | No log | 29.0 | 203 | 0.5804 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
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- | No log | 30.0 | 210 | 0.5806 | 0.8333 | 0.4167 | 0.5556 | 0.8621 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
88
 
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  ### Framework versions
 
20
 
21
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
22
  It achieves the following results on the evaluation set:
23
+ - Loss: 0.6021
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+ - Precision: 0.6429
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+ - Recall: 0.75
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+ - F1: 0.6923
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+ - Accuracy: 0.9310
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  ## Model description
30
 
 
43
  ### Training hyperparameters
44
 
45
  The following hyperparameters were used during training:
46
+ - learning_rate: 5e-05
47
  - train_batch_size: 8
48
  - eval_batch_size: 8
49
  - seed: 42
50
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
51
  - lr_scheduler_type: linear
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+ - num_epochs: 60
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54
  ### Training results
55
 
56
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
57
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 7 | 0.4718 | 0.7692 | 0.8333 | 0.8 | 0.9483 |
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+ | No log | 2.0 | 14 | 0.6303 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 3.0 | 21 | 0.6305 | 0.7692 | 0.8333 | 0.8 | 0.9310 |
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+ | No log | 4.0 | 28 | 0.6725 | 0.7692 | 0.8333 | 0.8 | 0.9310 |
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+ | No log | 5.0 | 35 | 0.7307 | 0.7692 | 0.8333 | 0.8 | 0.9310 |
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+ | No log | 6.0 | 42 | 0.6282 | 0.7692 | 0.8333 | 0.8 | 0.9483 |
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+ | No log | 7.0 | 49 | 0.9412 | 0.75 | 0.75 | 0.75 | 0.8793 |
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+ | No log | 8.0 | 56 | 0.9923 | 0.8 | 0.6667 | 0.7273 | 0.8966 |
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+ | No log | 9.0 | 63 | 1.0986 | 0.75 | 0.5 | 0.6 | 0.8966 |
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+ | No log | 10.0 | 70 | 0.7721 | 0.6154 | 0.6667 | 0.64 | 0.9138 |
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+ | No log | 11.0 | 77 | 0.5562 | 0.7692 | 0.8333 | 0.8 | 0.9483 |
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+ | No log | 12.0 | 84 | 0.5408 | 0.7692 | 0.8333 | 0.8 | 0.9483 |
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+ | No log | 13.0 | 91 | 0.8271 | 0.6667 | 0.6667 | 0.6667 | 0.9138 |
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+ | No log | 14.0 | 98 | 1.5415 | 0.875 | 0.5833 | 0.7000 | 0.8793 |
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+ | No log | 15.0 | 105 | 1.7072 | 0.8571 | 0.5 | 0.6316 | 0.8621 |
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+ | No log | 16.0 | 112 | 1.7351 | 0.8571 | 0.5 | 0.6316 | 0.8621 |
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+ | No log | 17.0 | 119 | 1.7483 | 0.8571 | 0.5 | 0.6316 | 0.8621 |
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+ | No log | 18.0 | 126 | 1.1396 | 0.7273 | 0.6667 | 0.6957 | 0.8793 |
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+ | No log | 19.0 | 133 | 0.7813 | 0.6667 | 0.6667 | 0.6667 | 0.8966 |
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+ | No log | 20.0 | 140 | 0.5685 | 0.7692 | 0.8333 | 0.8 | 0.9310 |
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+ | No log | 21.0 | 147 | 0.5689 | 0.7692 | 0.8333 | 0.8 | 0.9310 |
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+ | No log | 22.0 | 154 | 1.0915 | 0.7273 | 0.6667 | 0.6957 | 0.8966 |
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+ | No log | 23.0 | 161 | 0.6100 | 0.75 | 0.75 | 0.75 | 0.9310 |
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+ | No log | 24.0 | 168 | 0.6199 | 0.7143 | 0.8333 | 0.7692 | 0.9138 |
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+ | No log | 25.0 | 175 | 0.7130 | 0.7143 | 0.8333 | 0.7692 | 0.8966 |
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+ | No log | 26.0 | 182 | 0.5825 | 0.7692 | 0.8333 | 0.8 | 0.9310 |
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+ | No log | 27.0 | 189 | 0.5352 | 0.7692 | 0.8333 | 0.8 | 0.9310 |
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+ | No log | 28.0 | 196 | 0.5182 | 0.7692 | 0.8333 | 0.8 | 0.9310 |
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+ | No log | 29.0 | 203 | 0.5138 | 0.7692 | 0.8333 | 0.8 | 0.9310 |
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+ | No log | 30.0 | 210 | 0.5142 | 0.7692 | 0.8333 | 0.8 | 0.9483 |
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+ | No log | 31.0 | 217 | 0.5332 | 0.7692 | 0.8333 | 0.8 | 0.9483 |
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+ | No log | 32.0 | 224 | 0.5501 | 0.7692 | 0.8333 | 0.8 | 0.9483 |
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+ | No log | 33.0 | 231 | 0.5644 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 34.0 | 238 | 0.5722 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 35.0 | 245 | 0.5759 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 36.0 | 252 | 0.5776 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 37.0 | 259 | 0.5784 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 38.0 | 266 | 0.5788 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 39.0 | 273 | 0.5790 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 40.0 | 280 | 0.5786 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 41.0 | 287 | 0.5786 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 42.0 | 294 | 0.5787 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 43.0 | 301 | 0.5788 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 44.0 | 308 | 0.5790 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 45.0 | 315 | 0.6023 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 46.0 | 322 | 0.6284 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 47.0 | 329 | 0.6261 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 48.0 | 336 | 0.6254 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 49.0 | 343 | 0.6135 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 50.0 | 350 | 0.6077 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 51.0 | 357 | 0.6051 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 52.0 | 364 | 0.6041 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 53.0 | 371 | 0.6038 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 54.0 | 378 | 0.6034 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 55.0 | 385 | 0.6030 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 56.0 | 392 | 0.6030 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 57.0 | 399 | 0.6030 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 58.0 | 406 | 0.6024 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 59.0 | 413 | 0.6022 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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+ | No log | 60.0 | 420 | 0.6021 | 0.6429 | 0.75 | 0.6923 | 0.9310 |
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  ### Framework versions
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