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wzChen
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my_awesome_wnut_model

Token Classification
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distilbert
generated_from_trainer
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End of training

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  1. README.md +30 -12
  2. model.safetensors +1 -1
  3. runs/Mar17_13-54-37_46c7f6300639/events.out.tfevents.1710683678.46c7f6300639.34.3 +2 -2
README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.5817555938037866
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  - name: Recall
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  type: recall
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- value: 0.3132530120481928
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  - name: F1
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  type: f1
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- value: 0.40722891566265057
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  - name: Accuracy
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  type: accuracy
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- value: 0.9419434825360181
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the wnut_17 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2735
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- - Precision: 0.5818
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- - Recall: 0.3133
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- - F1: 0.4072
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- - Accuracy: 0.9419
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  ## Model description
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@@ -73,14 +73,32 @@ 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: 2
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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 | 213 | 0.2819 | 0.5 | 0.2502 | 0.3335 | 0.9385 |
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- | No log | 2.0 | 426 | 0.2735 | 0.5818 | 0.3133 | 0.4072 | 0.9419 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.558252427184466
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  - name: Recall
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  type: recall
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+ value: 0.4263206672845227
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  - name: F1
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  type: f1
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+ value: 0.48344718864950076
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9477576845795391
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the wnut_17 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4207
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+ - Precision: 0.5583
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+ - Recall: 0.4263
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+ - F1: 0.4834
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+ - Accuracy: 0.9478
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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: 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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+ | No log | 1.0 | 213 | 0.3267 | 0.5351 | 0.4235 | 0.4728 | 0.9472 |
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+ | No log | 2.0 | 426 | 0.3741 | 0.4730 | 0.3818 | 0.4226 | 0.9428 |
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+ | 0.0126 | 3.0 | 639 | 0.3431 | 0.5336 | 0.4189 | 0.4694 | 0.9466 |
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+ | 0.0126 | 4.0 | 852 | 0.3790 | 0.5983 | 0.3920 | 0.4737 | 0.9477 |
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+ | 0.008 | 5.0 | 1065 | 0.3610 | 0.5289 | 0.4328 | 0.4760 | 0.9472 |
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+ | 0.008 | 6.0 | 1278 | 0.3580 | 0.5637 | 0.4347 | 0.4908 | 0.9477 |
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+ | 0.008 | 7.0 | 1491 | 0.3569 | 0.5339 | 0.4458 | 0.4859 | 0.9474 |
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+ | 0.0049 | 8.0 | 1704 | 0.3988 | 0.5602 | 0.4013 | 0.4676 | 0.9470 |
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+ | 0.0049 | 9.0 | 1917 | 0.4180 | 0.5901 | 0.3976 | 0.4751 | 0.9471 |
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+ | 0.0032 | 10.0 | 2130 | 0.3969 | 0.5320 | 0.4161 | 0.4670 | 0.9468 |
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+ | 0.0032 | 11.0 | 2343 | 0.4265 | 0.5851 | 0.4013 | 0.4761 | 0.9473 |
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+ | 0.003 | 12.0 | 2556 | 0.4003 | 0.5569 | 0.4263 | 0.4829 | 0.9475 |
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+ | 0.003 | 13.0 | 2769 | 0.4234 | 0.5936 | 0.3967 | 0.4756 | 0.9480 |
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+ | 0.003 | 14.0 | 2982 | 0.4016 | 0.5482 | 0.4272 | 0.4802 | 0.9482 |
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+ | 0.002 | 15.0 | 3195 | 0.4312 | 0.5655 | 0.4041 | 0.4714 | 0.9471 |
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+ | 0.002 | 16.0 | 3408 | 0.4310 | 0.5611 | 0.4087 | 0.4729 | 0.9470 |
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+ | 0.0014 | 17.0 | 3621 | 0.4287 | 0.5556 | 0.4124 | 0.4734 | 0.9471 |
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+ | 0.0014 | 18.0 | 3834 | 0.4193 | 0.5572 | 0.4198 | 0.4789 | 0.9475 |
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+ | 0.0014 | 19.0 | 4047 | 0.4188 | 0.5583 | 0.4263 | 0.4834 | 0.9478 |
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+ | 0.0014 | 20.0 | 4260 | 0.4207 | 0.5583 | 0.4263 | 0.4834 | 0.9478 |
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  ### Framework versions
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