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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 -17
  2. model.safetensors +1 -1
  3. runs/Mar04_09-36-11_561d3d140928/events.out.tfevents.1709544986.561d3d140928.219.9 +3 -0
  4. runs/Mar04_09-36-43_561d3d140928/events.out.tfevents.1709545017.561d3d140928.219.10 +3 -0
  5. runs/Mar04_09-36-43_561d3d140928/events.out.tfevents.1709545037.561d3d140928.219.11 +3 -0
  6. 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: 0.5370
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- - Precision: 0.9
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- - Recall: 0.6923
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- - F1: 0.7826
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- - Accuracy: 0.9020
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  ## Model description
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@@ -43,28 +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: 5e-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: 10
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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 | 6 | 0.2277 | 0.9 | 0.6923 | 0.7826 | 0.9020 |
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- | No log | 2.0 | 12 | 0.4353 | 0.9 | 0.6923 | 0.7826 | 0.9020 |
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- | No log | 3.0 | 18 | 0.5766 | 0.9 | 0.6923 | 0.7826 | 0.9020 |
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- | No log | 4.0 | 24 | 0.5480 | 0.9 | 0.6923 | 0.7826 | 0.9020 |
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- | No log | 5.0 | 30 | 0.5291 | 0.9 | 0.6923 | 0.7826 | 0.9020 |
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- | No log | 6.0 | 36 | 0.5219 | 0.9 | 0.6923 | 0.7826 | 0.9020 |
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- | No log | 7.0 | 42 | 0.5224 | 0.9 | 0.6923 | 0.7826 | 0.9020 |
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- | No log | 8.0 | 48 | 0.5252 | 0.9 | 0.6923 | 0.7826 | 0.9020 |
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- | No log | 9.0 | 54 | 0.5330 | 0.9 | 0.6923 | 0.7826 | 0.9020 |
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- | No log | 10.0 | 60 | 0.5370 | 0.9 | 0.6923 | 0.7826 | 0.9020 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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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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  ### 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
49
  - seed: 42
50
  - 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: 60
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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 | 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
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