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bert-base-uncased-finetuned-ner

Token Classification
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  1. README.md +30 -17
  2. model.safetensors +1 -1
  3. runs/Aug30_15-42-45_66d12255e562/events.out.tfevents.1725032566.66d12255e562.293.0 +2 -2
README.md CHANGED
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  ---
 
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  license: apache-2.0
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  base_model: bert-base-uncased
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  tags:
@@ -20,11 +21,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2076
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- - Precision: 0.9531
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- - Recall: 0.9556
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- - F1: 0.9544
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- - Accuracy: 0.9468
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  ## Model description
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@@ -43,26 +44,38 @@ 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: 16
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- - eval_batch_size: 16
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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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- | 0.3298 | 1.0 | 1250 | 0.2486 | 0.9328 | 0.9362 | 0.9345 | 0.9253 |
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- | 0.1802 | 2.0 | 2500 | 0.2049 | 0.9489 | 0.9552 | 0.9520 | 0.9448 |
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- | 0.1233 | 3.0 | 3750 | 0.2076 | 0.9531 | 0.9556 | 0.9544 | 0.9468 |
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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- - Transformers 4.41.2
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- - Pytorch 2.3.0+cu121
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- - Datasets 2.19.2
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  - Tokenizers 0.19.1
 
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  ---
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+ library_name: transformers
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  license: apache-2.0
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  base_model: bert-base-uncased
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  tags:
 
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3910
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+ - Precision: 0.9616
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+ - Recall: 0.9637
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+ - F1: 0.9627
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+ - Accuracy: 0.9560
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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: 6
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+ - eval_batch_size: 6
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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: 15
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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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+ | 0.3052 | 1.0 | 3334 | 0.2630 | 0.9365 | 0.9367 | 0.9366 | 0.9228 |
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+ | 0.2104 | 2.0 | 6668 | 0.2481 | 0.9418 | 0.9537 | 0.9477 | 0.9400 |
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+ | 0.163 | 3.0 | 10002 | 0.2390 | 0.9495 | 0.9606 | 0.9550 | 0.9479 |
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+ | 0.1151 | 4.0 | 13336 | 0.2516 | 0.9549 | 0.9616 | 0.9583 | 0.9515 |
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+ | 0.0809 | 5.0 | 16670 | 0.2887 | 0.9590 | 0.9556 | 0.9573 | 0.9493 |
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+ | 0.0625 | 6.0 | 20004 | 0.2912 | 0.9573 | 0.9611 | 0.9592 | 0.9520 |
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+ | 0.0516 | 7.0 | 23338 | 0.3139 | 0.9581 | 0.9563 | 0.9572 | 0.9501 |
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+ | 0.0388 | 8.0 | 26672 | 0.3070 | 0.9605 | 0.9600 | 0.9602 | 0.9531 |
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+ | 0.0273 | 9.0 | 30006 | 0.3344 | 0.9607 | 0.9617 | 0.9612 | 0.9535 |
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+ | 0.0252 | 10.0 | 33340 | 0.3547 | 0.9608 | 0.9638 | 0.9623 | 0.9554 |
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+ | 0.0242 | 11.0 | 36674 | 0.3726 | 0.9600 | 0.9619 | 0.9610 | 0.9541 |
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+ | 0.0119 | 12.0 | 40008 | 0.3727 | 0.9602 | 0.9623 | 0.9612 | 0.9546 |
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+ | 0.0078 | 13.0 | 43342 | 0.3772 | 0.9617 | 0.9639 | 0.9628 | 0.9562 |
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+ | 0.0078 | 14.0 | 46676 | 0.3904 | 0.9615 | 0.9638 | 0.9627 | 0.9560 |
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+ | 0.0026 | 15.0 | 50010 | 0.3910 | 0.9616 | 0.9637 | 0.9627 | 0.9560 |
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  ### Framework versions
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+ - Transformers 4.44.2
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 2.21.0
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  - Tokenizers 0.19.1
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