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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 +65 -65
  2. model.safetensors +1 -1
  3. runs/Mar04_09-46-20_561d3d140928/events.out.tfevents.1709545590.561d3d140928.219.12 +3 -0
  4. runs/Mar04_09-46-20_561d3d140928/events.out.tfevents.1709545616.561d3d140928.219.13 +3 -0
  5. 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: 0.7333
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- - Precision: 0.8333
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- - Recall: 0.7692
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- - F1: 0.8
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- - Accuracy: 0.9412
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  ## Model description
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@@ -55,66 +55,66 @@ The following hyperparameters were used during training:
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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 | 6 | 0.8368 | 0.9 | 0.6923 | 0.7826 | 0.9020 |
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- | No log | 2.0 | 12 | 0.8605 | 0.9 | 0.6923 | 0.7826 | 0.9020 |
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- | No log | 3.0 | 18 | 0.9393 | 0.9 | 0.6923 | 0.7826 | 0.9020 |
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- | No log | 4.0 | 24 | 0.9558 | 0.9 | 0.6923 | 0.7826 | 0.9020 |
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- | No log | 5.0 | 30 | 0.8822 | 0.9 | 0.6923 | 0.7826 | 0.9020 |
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- | No log | 6.0 | 36 | 0.6826 | 0.9091 | 0.7692 | 0.8333 | 0.9216 |
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- | No log | 7.0 | 42 | 0.6756 | 0.9091 | 0.7692 | 0.8333 | 0.9216 |
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- | No log | 8.0 | 48 | 0.6656 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 9.0 | 54 | 0.6710 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 10.0 | 60 | 0.6788 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 11.0 | 66 | 0.6885 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 12.0 | 72 | 0.6846 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 13.0 | 78 | 0.6872 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 14.0 | 84 | 0.6882 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 15.0 | 90 | 0.6901 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 16.0 | 96 | 0.6934 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 17.0 | 102 | 0.6967 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 18.0 | 108 | 0.6986 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 19.0 | 114 | 0.7007 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 20.0 | 120 | 0.7013 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 21.0 | 126 | 0.7016 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 22.0 | 132 | 0.7030 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 23.0 | 138 | 0.7049 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 24.0 | 144 | 0.7076 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 25.0 | 150 | 0.7109 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 26.0 | 156 | 0.7136 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 27.0 | 162 | 0.7166 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 28.0 | 168 | 0.7188 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 29.0 | 174 | 0.7198 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 30.0 | 180 | 0.7208 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 31.0 | 186 | 0.7219 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 32.0 | 192 | 0.7232 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 33.0 | 198 | 0.7244 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 34.0 | 204 | 0.7256 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 35.0 | 210 | 0.7276 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 36.0 | 216 | 0.7292 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 37.0 | 222 | 0.7297 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 38.0 | 228 | 0.7298 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 39.0 | 234 | 0.7295 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 40.0 | 240 | 0.7293 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 41.0 | 246 | 0.7294 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 42.0 | 252 | 0.7304 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 43.0 | 258 | 0.7315 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 44.0 | 264 | 0.7323 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 45.0 | 270 | 0.7329 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 46.0 | 276 | 0.7332 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 47.0 | 282 | 0.7335 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 48.0 | 288 | 0.7339 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 49.0 | 294 | 0.7342 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 50.0 | 300 | 0.7345 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 51.0 | 306 | 0.7350 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 52.0 | 312 | 0.7356 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 53.0 | 318 | 0.7361 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 54.0 | 324 | 0.7342 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 55.0 | 330 | 0.7334 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 56.0 | 336 | 0.7331 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 57.0 | 342 | 0.7331 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 58.0 | 348 | 0.7332 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 59.0 | 354 | 0.7332 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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- | No log | 60.0 | 360 | 0.7333 | 0.8333 | 0.7692 | 0.8 | 0.9412 |
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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.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0047
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+ - Precision: 1.0
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+ - Recall: 1.0
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+ - F1: 1.0
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+ - Accuracy: 1.0
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  ## Model description
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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 | 6 | 0.6465 | 0.0 | 0.0 | 0.0 | 0.7429 |
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+ | No log | 2.0 | 12 | 0.5074 | 0.0 | 0.0 | 0.0 | 0.7429 |
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+ | No log | 3.0 | 18 | 0.3464 | 0.6 | 0.375 | 0.4615 | 0.8571 |
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+ | No log | 4.0 | 24 | 0.2325 | 0.6667 | 0.5 | 0.5714 | 0.8857 |
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+ | No log | 5.0 | 30 | 0.1652 | 0.75 | 0.75 | 0.75 | 0.9429 |
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+ | No log | 6.0 | 36 | 0.1230 | 0.7778 | 0.875 | 0.8235 | 0.9714 |
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+ | No log | 7.0 | 42 | 0.0933 | 0.7778 | 0.875 | 0.8235 | 0.9714 |
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+ | No log | 8.0 | 48 | 0.0789 | 0.7778 | 0.875 | 0.8235 | 0.9714 |
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+ | No log | 9.0 | 54 | 0.0681 | 0.7778 | 0.875 | 0.8235 | 0.9714 |
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+ | No log | 10.0 | 60 | 0.0519 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 11.0 | 66 | 0.0395 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 12.0 | 72 | 0.0309 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 13.0 | 78 | 0.0250 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 14.0 | 84 | 0.0208 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 15.0 | 90 | 0.0179 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 16.0 | 96 | 0.0154 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 17.0 | 102 | 0.0136 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 18.0 | 108 | 0.0123 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 19.0 | 114 | 0.0115 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 20.0 | 120 | 0.0107 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 21.0 | 126 | 0.0100 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 22.0 | 132 | 0.0095 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 23.0 | 138 | 0.0091 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 24.0 | 144 | 0.0086 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 25.0 | 150 | 0.0082 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 26.0 | 156 | 0.0079 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 27.0 | 162 | 0.0076 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 28.0 | 168 | 0.0074 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 29.0 | 174 | 0.0077 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 30.0 | 180 | 0.0080 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 31.0 | 186 | 0.0081 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 32.0 | 192 | 0.0077 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 33.0 | 198 | 0.0067 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 34.0 | 204 | 0.0062 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 35.0 | 210 | 0.0057 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 36.0 | 216 | 0.0055 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 37.0 | 222 | 0.0054 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 38.0 | 228 | 0.0063 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 39.0 | 234 | 0.0070 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 40.0 | 240 | 0.0070 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 41.0 | 246 | 0.0069 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 42.0 | 252 | 0.0067 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 43.0 | 258 | 0.0065 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 44.0 | 264 | 0.0062 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 45.0 | 270 | 0.0060 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 46.0 | 276 | 0.0058 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 47.0 | 282 | 0.0057 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 48.0 | 288 | 0.0056 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 49.0 | 294 | 0.0055 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 50.0 | 300 | 0.0054 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 51.0 | 306 | 0.0051 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 52.0 | 312 | 0.0050 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 53.0 | 318 | 0.0049 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 54.0 | 324 | 0.0048 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 55.0 | 330 | 0.0048 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 56.0 | 336 | 0.0047 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 57.0 | 342 | 0.0047 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 58.0 | 348 | 0.0047 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 59.0 | 354 | 0.0047 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 60.0 | 360 | 0.0047 | 1.0 | 1.0 | 1.0 | 1.0 |
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  ### Framework versions
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