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--- |
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license: apache-2.0 |
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base_model: distilbert/distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- wnut_17 |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: my_awesome_wnut_model |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: wnut_17 |
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type: wnut_17 |
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config: wnut_17 |
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split: test |
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args: wnut_17 |
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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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should probably proofread and complete it, then remove this comment. --> |
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# my_awesome_wnut_model |
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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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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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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: 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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- Transformers 4.38.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.2 |
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