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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- conll2003 |
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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: ner_conll2003 |
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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: conll2003 |
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type: conll2003 |
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args: conll2003 |
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metric: |
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name: Accuracy |
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type: accuracy |
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value: 0.9772880710440217 |
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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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# ner_conll2003 |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1495 |
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- Precision: 0.8985 |
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- Recall: 0.9130 |
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- F1: 0.9057 |
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- Accuracy: 0.9773 |
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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: 3e-05 |
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- train_batch_size: 16 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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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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| 0.423 | 1.0 | 877 | 0.0656 | 0.9158 | 0.9268 | 0.9213 | 0.9818 | |
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| 0.0575 | 2.0 | 1754 | 0.0574 | 0.9285 | 0.9445 | 0.9364 | 0.9847 | |
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| 0.0295 | 3.0 | 2631 | 0.0631 | 0.9414 | 0.9456 | 0.9435 | 0.9859 | |
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| 0.0155 | 4.0 | 3508 | 0.0680 | 0.9395 | 0.9467 | 0.9431 | 0.9860 | |
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| 0.0097 | 5.0 | 4385 | 0.0694 | 0.9385 | 0.9513 | 0.9449 | 0.9863 | |
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| 0.0059 | 6.0 | 5262 | 0.0743 | 0.9363 | 0.9471 | 0.9416 | 0.9860 | |
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| 0.0041 | 7.0 | 6139 | 0.0803 | 0.9371 | 0.9518 | 0.9444 | 0.9862 | |
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### Framework versions |
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- Transformers 4.9.1 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.11.0 |
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- Tokenizers 0.10.2 |
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