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sickcell
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bert-finetuned-ner

Token Classification
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bert
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  1. README.md +29 -12
  2. runs/Jul29_07-46-12_9b0aa93e5939/events.out.tfevents.1722239175.9b0aa93e5939.759.2 +2 -2
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 [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4022
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- - Precision: 0.7421
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- - Recall: 0.7951
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- - F1: 0.7677
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- - Accuracy: 0.8883
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  ## Model description
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@@ -43,21 +43,38 @@ 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: 3
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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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- | 0.7548 | 1.0 | 680 | 0.4630 | 0.6857 | 0.7150 | 0.7000 | 0.8615 |
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- | 0.4124 | 2.0 | 1360 | 0.4143 | 0.7299 | 0.7698 | 0.7493 | 0.8786 |
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- | 0.2546 | 3.0 | 2040 | 0.4022 | 0.7421 | 0.7951 | 0.7677 | 0.8883 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5707
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+ - Precision: 0.8258
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+ - Recall: 0.8753
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+ - F1: 0.8498
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+ - Accuracy: 0.9254
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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: 1e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
49
  - 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: 20
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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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+ | 0.1966 | 1.0 | 680 | 0.3961 | 0.7705 | 0.8096 | 0.7896 | 0.8971 |
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+ | 0.1487 | 2.0 | 1360 | 0.4435 | 0.7607 | 0.8258 | 0.7919 | 0.8950 |
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+ | 0.1258 | 3.0 | 2040 | 0.4180 | 0.7727 | 0.8450 | 0.8072 | 0.9052 |
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+ | 0.1163 | 4.0 | 2720 | 0.4227 | 0.7890 | 0.8418 | 0.8145 | 0.9078 |
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+ | 0.1019 | 5.0 | 3400 | 0.4721 | 0.7664 | 0.8630 | 0.8118 | 0.9068 |
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+ | 0.0763 | 6.0 | 4080 | 0.4444 | 0.8033 | 0.8542 | 0.8280 | 0.9172 |
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+ | 0.0641 | 7.0 | 4760 | 0.4551 | 0.8189 | 0.8550 | 0.8366 | 0.9179 |
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+ | 0.0615 | 8.0 | 5440 | 0.4615 | 0.8167 | 0.8640 | 0.8397 | 0.9202 |
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+ | 0.0468 | 9.0 | 6120 | 0.4922 | 0.8104 | 0.8668 | 0.8376 | 0.9205 |
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+ | 0.0381 | 10.0 | 6800 | 0.4991 | 0.8213 | 0.8642 | 0.8422 | 0.9210 |
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+ | 0.0381 | 11.0 | 7480 | 0.4950 | 0.8298 | 0.8593 | 0.8443 | 0.9230 |
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+ | 0.0289 | 12.0 | 8160 | 0.5273 | 0.8220 | 0.8717 | 0.8461 | 0.9232 |
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+ | 0.026 | 13.0 | 8840 | 0.5366 | 0.8207 | 0.8700 | 0.8446 | 0.9230 |
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+ | 0.0227 | 14.0 | 9520 | 0.5466 | 0.8240 | 0.8698 | 0.8463 | 0.9230 |
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+ | 0.02 | 15.0 | 10200 | 0.5530 | 0.8250 | 0.8710 | 0.8474 | 0.9247 |
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+ | 0.0173 | 16.0 | 10880 | 0.5571 | 0.8235 | 0.8726 | 0.8473 | 0.9244 |
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+ | 0.0152 | 17.0 | 11560 | 0.5551 | 0.8282 | 0.8700 | 0.8486 | 0.9248 |
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+ | 0.0146 | 18.0 | 12240 | 0.5686 | 0.8270 | 0.8727 | 0.8492 | 0.9246 |
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+ | 0.0142 | 19.0 | 12920 | 0.5687 | 0.8258 | 0.8753 | 0.8498 | 0.9259 |
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+ | 0.0135 | 20.0 | 13600 | 0.5707 | 0.8258 | 0.8753 | 0.8498 | 0.9254 |
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  ### Framework versions
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