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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google-bert/bert-base-uncased |
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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: g-bert-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: test |
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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: 0.8925347222222222 |
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- name: Recall |
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type: recall |
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value: 0.9102337110481586 |
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- name: F1 |
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type: f1 |
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value: 0.901297335203366 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9799720038763864 |
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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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# g-bert-NER |
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/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: 0.1387 |
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- Precision: 0.8925 |
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- Recall: 0.9102 |
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- F1: 0.9013 |
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- Accuracy: 0.9800 |
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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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| 0.1773 | 1.0 | 878 | 0.1028 | 0.8910 | 0.8947 | 0.8928 | 0.9781 | |
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| 0.036 | 2.0 | 1756 | 0.1125 | 0.8901 | 0.9132 | 0.9015 | 0.9793 | |
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| 0.0194 | 3.0 | 2634 | 0.1202 | 0.8948 | 0.9093 | 0.9020 | 0.9800 | |
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| 0.0112 | 4.0 | 3512 | 0.1346 | 0.8889 | 0.9136 | 0.9011 | 0.9794 | |
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| 0.0081 | 5.0 | 4390 | 0.1387 | 0.8925 | 0.9102 | 0.9013 | 0.9800 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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