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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-large-cased |
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tags: |
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- generated_from_trainer |
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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-large-cased-finetuned-ner-geocorpus |
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results: [] |
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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-large-cased-finetuned-ner-geocorpus |
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This model is a fine-tuned version of [google-bert/bert-large-cased](https://huggingface.co/google-bert/bert-large-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1330 |
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- Precision: 0.868 |
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- Recall: 0.8872 |
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- F1: 0.8775 |
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- Accuracy: 0.9793 |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 16 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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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 | 0.9991 | 275 | 0.1429 | 0.7212 | 0.7318 | 0.7265 | 0.9613 | |
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| 0.21 | 1.9982 | 550 | 0.1111 | 0.7211 | 0.8267 | 0.7703 | 0.9654 | |
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| 0.21 | 2.9973 | 825 | 0.0979 | 0.8168 | 0.8168 | 0.8168 | 0.9725 | |
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| 0.0651 | 4.0 | 1101 | 0.1088 | 0.7574 | 0.9011 | 0.8230 | 0.9678 | |
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| 0.0651 | 4.9991 | 1376 | 0.1033 | 0.825 | 0.8904 | 0.8565 | 0.9744 | |
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| 0.0305 | 5.9982 | 1651 | 0.1132 | 0.8908 | 0.8536 | 0.8718 | 0.9785 | |
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| 0.0305 | 6.9973 | 1926 | 0.1127 | 0.8591 | 0.8823 | 0.8705 | 0.9786 | |
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| 0.0153 | 8.0 | 2202 | 0.1155 | 0.8687 | 0.8814 | 0.8750 | 0.9795 | |
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| 0.0153 | 8.9991 | 2477 | 0.1280 | 0.8860 | 0.8774 | 0.8817 | 0.9804 | |
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| 0.0089 | 9.9909 | 2750 | 0.1330 | 0.868 | 0.8872 | 0.8775 | 0.9793 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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