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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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- 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: bert-tiny-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: conll2003 |
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type: conll2003 |
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args: conll2003 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8083060109289617 |
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- name: Recall |
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type: recall |
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value: 0.8273856136033113 |
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- name: F1 |
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type: f1 |
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value: 0.8177345348001547 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9597597979252387 |
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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-tiny-finetuned-ner |
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This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1689 |
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- Precision: 0.8083 |
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- Recall: 0.8274 |
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- F1: 0.8177 |
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- Accuracy: 0.9598 |
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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: 5 |
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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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| 0.0355 | 1.0 | 878 | 0.1692 | 0.8072 | 0.8248 | 0.8159 | 0.9594 | |
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| 0.0411 | 2.0 | 1756 | 0.1678 | 0.8101 | 0.8277 | 0.8188 | 0.9600 | |
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| 0.0386 | 3.0 | 2634 | 0.1697 | 0.8103 | 0.8269 | 0.8186 | 0.9599 | |
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| 0.0373 | 4.0 | 3512 | 0.1694 | 0.8106 | 0.8263 | 0.8183 | 0.9600 | |
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| 0.0383 | 5.0 | 4390 | 0.1689 | 0.8083 | 0.8274 | 0.8177 | 0.9598 | |
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### Framework versions |
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- Transformers 4.10.0 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.11.0 |
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- Tokenizers 0.10.3 |
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