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--- |
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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: bert-finetuned-ner |
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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: validation |
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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: 0.6274509803921569 |
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- name: Recall |
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type: recall |
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value: 0.49760765550239233 |
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- name: F1 |
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type: f1 |
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value: 0.5550366911274184 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9333784769246797 |
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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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# bert-finetuned-ner |
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This model was trained from scratch on the wnut_17 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4590 |
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- Precision: 0.6275 |
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- Recall: 0.4976 |
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- F1: 0.5550 |
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- Accuracy: 0.9334 |
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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: 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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| No log | 1.0 | 425 | 0.4576 | 0.6556 | 0.4713 | 0.5484 | 0.9321 | |
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| 0.0403 | 2.0 | 850 | 0.4647 | 0.6293 | 0.4629 | 0.5334 | 0.9311 | |
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| 0.0227 | 3.0 | 1275 | 0.4590 | 0.6275 | 0.4976 | 0.5550 | 0.9334 | |
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### Framework versions |
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- Transformers 4.27.0.dev0 |
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- Pytorch 2.0.1 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.2 |
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