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--- |
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license: mit |
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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-to-distilbert-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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config: conll2003 |
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split: train |
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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: 0.014729299363057325 |
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- name: Recall |
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type: recall |
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value: 0.018680578929653316 |
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- name: F1 |
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type: f1 |
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value: 0.016471286541029827 |
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- name: Accuracy |
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type: accuracy |
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value: 0.7599340672278802 |
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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-to-distilbert-NER |
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This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 43.2398 |
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- Precision: 0.0147 |
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- Recall: 0.0187 |
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- F1: 0.0165 |
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- Accuracy: 0.7599 |
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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: 6e-05 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 33 |
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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: 15 |
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- mixed_precision_training: Native AMP |
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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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| 190.2685 | 1.0 | 110 | 127.2351 | 0.0157 | 0.0098 | 0.0120 | 0.7569 | |
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| 105.4389 | 2.0 | 220 | 97.1100 | 0.0281 | 0.0298 | 0.0289 | 0.7587 | |
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| 77.0337 | 3.0 | 330 | 76.9433 | 0.0136 | 0.0173 | 0.0152 | 0.7615 | |
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| 60.3477 | 4.0 | 440 | 65.9181 | 0.0130 | 0.0158 | 0.0143 | 0.7603 | |
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| 50.4086 | 5.0 | 550 | 58.5255 | 0.0170 | 0.0220 | 0.0192 | 0.7603 | |
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| 43.298 | 6.0 | 660 | 54.5405 | 0.0144 | 0.0187 | 0.0163 | 0.7594 | |
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| 39.0911 | 7.0 | 770 | 52.4767 | 0.0155 | 0.0195 | 0.0172 | 0.7613 | |
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| 35.07 | 8.0 | 880 | 49.1975 | 0.0170 | 0.0219 | 0.0192 | 0.7602 | |
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| 32.215 | 9.0 | 990 | 47.4422 | 0.0144 | 0.0187 | 0.0163 | 0.7599 | |
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| 29.9923 | 10.0 | 1100 | 46.5558 | 0.0167 | 0.0212 | 0.0187 | 0.7606 | |
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| 28.3599 | 11.0 | 1210 | 45.6301 | 0.0171 | 0.0214 | 0.0190 | 0.7613 | |
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| 26.8163 | 12.0 | 1320 | 45.0483 | 0.0141 | 0.0177 | 0.0157 | 0.7606 | |
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| 25.7434 | 13.0 | 1430 | 44.0639 | 0.0176 | 0.0222 | 0.0196 | 0.7605 | |
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| 24.9853 | 14.0 | 1540 | 43.6618 | 0.0148 | 0.0187 | 0.0165 | 0.7606 | |
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| 24.3179 | 15.0 | 1650 | 43.2398 | 0.0147 | 0.0187 | 0.0165 | 0.7599 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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