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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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- null |
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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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language: |
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- sr |
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model_index: |
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- name: bert-srb-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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metric: |
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name: Accuracy |
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type: accuracy |
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value: 0.9641060273510046 |
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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-srb-ner |
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This model was finetuned from Aleksandar/bert-srb-cased-oscar on the setimes.SR dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1647 |
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- Precision: 0.8247 |
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- Recall: 0.8454 |
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- F1: 0.8349 |
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- Accuracy: 0.9641 |
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## Model description |
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Default settings for BERT model, finetuned with batch size of 16. |
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## Intended uses & limitations |
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| Tag (IOB) | Numerical representation | Meaning (Beginning = B., Inside = I.) | |
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|-------------|--------------------------|------------------------------------------| |
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| O | 0 | Other | |
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| B-per | 1 | B.Person | |
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| I-per | 2 | I. Person | |
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| B-org | 3 | B. organization | |
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| I-org | 4 | I. organization | |
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| B-loc | 5 | B. location | |
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| I-loc | 6 | I. location | |
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| B-misc | 7 | B. Miscellaneous | |
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| I-misc | 8 | I. Miscellaneous | |
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| B-deriv-per | 9 | B. Derived Person | |
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MIT license |
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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: 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: 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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| No log | 1.0 | 207 | 0.2040 | 0.7006 | 0.7466 | 0.7228 | 0.9411 | |
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| No log | 2.0 | 414 | 0.1561 | 0.7299 | 0.7868 | 0.7573 | 0.9519 | |
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| 0.2313 | 3.0 | 621 | 0.1455 | 0.7693 | 0.7992 | 0.7840 | 0.9567 | |
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| 0.2313 | 4.0 | 828 | 0.1628 | 0.7760 | 0.8037 | 0.7896 | 0.9570 | |
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| 0.0828 | 5.0 | 1035 | 0.1424 | 0.7997 | 0.8299 | 0.8145 | 0.9604 | |
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| 0.0828 | 6.0 | 1242 | 0.1512 | 0.7983 | 0.8361 | 0.8168 | 0.9618 | |
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| 0.0828 | 7.0 | 1449 | 0.1587 | 0.8084 | 0.8415 | 0.8246 | 0.9627 | |
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| 0.0362 | 8.0 | 1656 | 0.1613 | 0.8154 | 0.8358 | 0.8255 | 0.9632 | |
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| 0.0362 | 9.0 | 1863 | 0.1685 | 0.8211 | 0.8429 | 0.8319 | 0.9632 | |
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| 0.0174 | 10.0 | 2070 | 0.1647 | 0.8247 | 0.8454 | 0.8349 | 0.9641 | |
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### Framework versions |
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- Transformers 4.9.2 |
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- Pytorch 1.9.0 |
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- Datasets 1.11.0 |
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- Tokenizers 0.10.1 |
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