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End of training

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  1. README.md +43 -35
  2. model.safetensors +1 -1
  3. runs/Apr03_15-46-05_d508c0ef2f9f/events.out.tfevents.1712159217.d508c0ef2f9f.9555.0 +3 -0
  4. runs/Apr03_15-46-05_d508c0ef2f9f/events.out.tfevents.1712174436.d508c0ef2f9f.9555.1 +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-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0935
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- - Precision: 0.8398
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- - Recall: 0.8319
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- - F1: 0.8316
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- - Accuracy: 0.8319
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  ## Model description
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@@ -49,41 +49,49 @@ The following hyperparameters were used during training:
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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: 4
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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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- | 0.0672 | 0.14 | 30 | 0.9287 | 0.8186 | 0.8123 | 0.8094 | 0.8123 |
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- | 0.0262 | 0.27 | 60 | 1.0312 | 0.8147 | 0.8067 | 0.8077 | 0.8067 |
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- | 0.1314 | 0.41 | 90 | 0.9548 | 0.8156 | 0.8123 | 0.8122 | 0.8123 |
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- | 0.0639 | 0.54 | 120 | 0.9111 | 0.8298 | 0.8207 | 0.8195 | 0.8207 |
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- | 0.0598 | 0.68 | 150 | 1.1390 | 0.8145 | 0.8011 | 0.8022 | 0.8011 |
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- | 0.1492 | 0.81 | 180 | 0.9996 | 0.8365 | 0.8319 | 0.8315 | 0.8319 |
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- | 0.1065 | 0.95 | 210 | 1.0810 | 0.8316 | 0.8179 | 0.8167 | 0.8179 |
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- | 0.0976 | 1.08 | 240 | 1.0728 | 0.8315 | 0.8263 | 0.8257 | 0.8263 |
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- | 0.0618 | 1.22 | 270 | 1.0524 | 0.8414 | 0.8319 | 0.8319 | 0.8319 |
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- | 0.0901 | 1.35 | 300 | 1.0568 | 0.8251 | 0.8151 | 0.8163 | 0.8151 |
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- | 0.0471 | 1.49 | 330 | 1.0259 | 0.8292 | 0.8179 | 0.8184 | 0.8179 |
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- | 0.028 | 1.62 | 360 | 1.0824 | 0.8284 | 0.8235 | 0.8223 | 0.8235 |
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- | 0.0334 | 1.76 | 390 | 1.1277 | 0.8264 | 0.8179 | 0.8178 | 0.8179 |
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- | 0.0136 | 1.89 | 420 | 1.0544 | 0.8379 | 0.8291 | 0.8270 | 0.8291 |
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- | 0.0134 | 2.03 | 450 | 1.0709 | 0.8408 | 0.8347 | 0.8327 | 0.8347 |
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- | 0.0323 | 2.16 | 480 | 1.1359 | 0.8399 | 0.8263 | 0.8261 | 0.8263 |
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- | 0.0182 | 2.3 | 510 | 1.1279 | 0.8344 | 0.8235 | 0.8243 | 0.8235 |
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- | 0.0211 | 2.43 | 540 | 1.0813 | 0.8443 | 0.8375 | 0.8380 | 0.8375 |
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- | 0.0009 | 2.57 | 570 | 1.1202 | 0.8399 | 0.8319 | 0.8318 | 0.8319 |
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- | 0.0227 | 2.7 | 600 | 1.1772 | 0.8255 | 0.8151 | 0.8153 | 0.8151 |
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- | 0.0007 | 2.84 | 630 | 1.1644 | 0.8299 | 0.8179 | 0.8182 | 0.8179 |
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- | 0.0017 | 2.97 | 660 | 1.1172 | 0.8406 | 0.8319 | 0.8311 | 0.8319 |
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- | 0.0005 | 3.11 | 690 | 1.1139 | 0.8403 | 0.8319 | 0.8311 | 0.8319 |
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- | 0.0094 | 3.24 | 720 | 1.0901 | 0.8363 | 0.8291 | 0.8287 | 0.8291 |
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- | 0.0011 | 3.38 | 750 | 1.0993 | 0.8436 | 0.8347 | 0.8349 | 0.8347 |
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- | 0.0005 | 3.51 | 780 | 1.0997 | 0.8436 | 0.8347 | 0.8349 | 0.8347 |
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- | 0.0005 | 3.65 | 810 | 1.1004 | 0.8372 | 0.8291 | 0.8288 | 0.8291 |
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- | 0.0005 | 3.78 | 840 | 1.1025 | 0.8372 | 0.8291 | 0.8288 | 0.8291 |
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- | 0.0025 | 3.92 | 870 | 1.0964 | 0.8372 | 0.8291 | 0.8288 | 0.8291 |
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.0027
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+ - Precision: 0.8141
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+ - Recall: 0.8067
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+ - F1: 0.8073
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+ - Accuracy: 0.8067
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  ## Model description
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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: 5
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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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+ | 1.9463 | 0.14 | 30 | 1.8631 | 0.1245 | 0.1625 | 0.0819 | 0.1625 |
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+ | 1.7589 | 0.27 | 60 | 1.4567 | 0.4725 | 0.5098 | 0.4483 | 0.5098 |
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+ | 1.389 | 0.41 | 90 | 1.2228 | 0.6230 | 0.5714 | 0.5547 | 0.5714 |
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+ | 1.2009 | 0.54 | 120 | 1.0306 | 0.7264 | 0.6835 | 0.6666 | 0.6835 |
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+ | 1.0999 | 0.68 | 150 | 0.8052 | 0.7808 | 0.7647 | 0.7625 | 0.7647 |
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+ | 0.8848 | 0.81 | 180 | 0.7826 | 0.7499 | 0.7283 | 0.7191 | 0.7283 |
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+ | 0.685 | 0.95 | 210 | 0.7337 | 0.7765 | 0.7591 | 0.7587 | 0.7591 |
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+ | 0.5562 | 1.08 | 240 | 0.6653 | 0.7897 | 0.7871 | 0.7863 | 0.7871 |
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+ | 0.4662 | 1.22 | 270 | 0.7158 | 0.7895 | 0.7535 | 0.7539 | 0.7535 |
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+ | 0.3985 | 1.35 | 300 | 0.6552 | 0.8160 | 0.8011 | 0.8024 | 0.8011 |
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+ | 0.317 | 1.49 | 330 | 0.7378 | 0.7902 | 0.7843 | 0.7836 | 0.7843 |
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+ | 0.4177 | 1.62 | 360 | 0.6983 | 0.8085 | 0.8039 | 0.8028 | 0.8039 |
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+ | 0.383 | 1.76 | 390 | 0.7612 | 0.7979 | 0.7759 | 0.7640 | 0.7759 |
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+ | 0.2906 | 1.89 | 420 | 0.7369 | 0.7914 | 0.7759 | 0.7761 | 0.7759 |
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+ | 0.3305 | 2.03 | 450 | 0.7302 | 0.7904 | 0.7787 | 0.7791 | 0.7787 |
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+ | 0.1398 | 2.16 | 480 | 0.7798 | 0.8169 | 0.8095 | 0.8084 | 0.8095 |
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+ | 0.0988 | 2.3 | 510 | 0.9284 | 0.7902 | 0.7815 | 0.7799 | 0.7815 |
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+ | 0.1449 | 2.43 | 540 | 0.8863 | 0.8196 | 0.8123 | 0.8133 | 0.8123 |
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+ | 0.2552 | 2.57 | 570 | 0.8396 | 0.8227 | 0.8179 | 0.8177 | 0.8179 |
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+ | 0.1616 | 2.7 | 600 | 0.8182 | 0.8172 | 0.8123 | 0.8128 | 0.8123 |
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+ | 0.2163 | 2.84 | 630 | 0.8075 | 0.8031 | 0.7983 | 0.7994 | 0.7983 |
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+ | 0.2134 | 2.97 | 660 | 0.9430 | 0.8190 | 0.8067 | 0.8080 | 0.8067 |
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+ | 0.1255 | 3.11 | 690 | 0.8907 | 0.8166 | 0.8123 | 0.8116 | 0.8123 |
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+ | 0.0969 | 3.24 | 720 | 0.8805 | 0.8009 | 0.7983 | 0.7977 | 0.7983 |
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+ | 0.0649 | 3.38 | 750 | 0.9065 | 0.7957 | 0.7843 | 0.7846 | 0.7843 |
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+ | 0.0328 | 3.51 | 780 | 0.9083 | 0.8141 | 0.8095 | 0.8093 | 0.8095 |
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+ | 0.0274 | 3.65 | 810 | 0.8894 | 0.8096 | 0.8011 | 0.8011 | 0.8011 |
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+ | 0.0906 | 3.78 | 840 | 0.9425 | 0.8166 | 0.8095 | 0.8101 | 0.8095 |
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+ | 0.0906 | 3.92 | 870 | 0.9333 | 0.8066 | 0.8011 | 0.8011 | 0.8011 |
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+ | 0.0641 | 4.05 | 900 | 0.9052 | 0.8108 | 0.8067 | 0.8063 | 0.8067 |
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+ | 0.0246 | 4.19 | 930 | 0.9993 | 0.8017 | 0.7955 | 0.7946 | 0.7955 |
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+ | 0.0551 | 4.32 | 960 | 0.9899 | 0.8174 | 0.8123 | 0.8122 | 0.8123 |
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+ | 0.0084 | 4.46 | 990 | 0.9954 | 0.8127 | 0.8067 | 0.8066 | 0.8067 |
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+ | 0.0049 | 4.59 | 1020 | 0.9912 | 0.8145 | 0.8095 | 0.8093 | 0.8095 |
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+ | 0.0217 | 4.73 | 1050 | 0.9957 | 0.8128 | 0.8067 | 0.8067 | 0.8067 |
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+ | 0.0144 | 4.86 | 1080 | 1.0042 | 0.8164 | 0.8095 | 0.8100 | 0.8095 |
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+ | 0.0276 | 5.0 | 1110 | 1.0027 | 0.8141 | 0.8067 | 0.8073 | 0.8067 |
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  ### Framework versions
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