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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 +33 -63
  2. model.safetensors +1 -1
  3. runs/Mar04_09-49-13_561d3d140928/events.out.tfevents.1709545763.561d3d140928.219.14 +3 -0
  4. runs/Mar04_09-49-13_561d3d140928/events.out.tfevents.1709545775.561d3d140928.219.15 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
@@ -20,7 +20,7 @@ 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: 0.0047
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  - Precision: 1.0
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  - Recall: 1.0
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  - F1: 1.0
@@ -43,78 +43,48 @@ 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: 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 | 0.6465 | 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
 
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: 0.0001
24
  - Precision: 1.0
25
  - Recall: 1.0
26
  - F1: 1.0
 
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
52
+ - num_epochs: 30
53
 
54
  ### Training results
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56
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
57
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 6 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 2.0 | 12 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 3.0 | 18 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 4.0 | 24 | 0.0087 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 5.0 | 30 | 0.1829 | 1.0 | 0.875 | 0.9333 | 0.9714 |
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+ | No log | 6.0 | 36 | 0.0092 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 7.0 | 42 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 8.0 | 48 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 9.0 | 54 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 10.0 | 60 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 11.0 | 66 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 12.0 | 72 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 13.0 | 78 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 14.0 | 84 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 15.0 | 90 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 16.0 | 96 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 17.0 | 102 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 18.0 | 108 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 19.0 | 114 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 20.0 | 120 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 21.0 | 126 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 22.0 | 132 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 23.0 | 138 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 24.0 | 144 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 25.0 | 150 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 26.0 | 156 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 27.0 | 162 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 28.0 | 168 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 29.0 | 174 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 30.0 | 180 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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