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--- |
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license: apache-2.0 |
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base_model: bert-base-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-finetuned-ner |
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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-finetuned-ner |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5707 |
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- Precision: 0.8258 |
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- Recall: 0.8753 |
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- F1: 0.8498 |
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- Accuracy: 0.9254 |
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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: 1e-05 |
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- train_batch_size: 8 |
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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: 20 |
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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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| 0.1966 | 1.0 | 680 | 0.3961 | 0.7705 | 0.8096 | 0.7896 | 0.8971 | |
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| 0.1487 | 2.0 | 1360 | 0.4435 | 0.7607 | 0.8258 | 0.7919 | 0.8950 | |
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| 0.1258 | 3.0 | 2040 | 0.4180 | 0.7727 | 0.8450 | 0.8072 | 0.9052 | |
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| 0.1163 | 4.0 | 2720 | 0.4227 | 0.7890 | 0.8418 | 0.8145 | 0.9078 | |
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| 0.1019 | 5.0 | 3400 | 0.4721 | 0.7664 | 0.8630 | 0.8118 | 0.9068 | |
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| 0.0763 | 6.0 | 4080 | 0.4444 | 0.8033 | 0.8542 | 0.8280 | 0.9172 | |
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| 0.0641 | 7.0 | 4760 | 0.4551 | 0.8189 | 0.8550 | 0.8366 | 0.9179 | |
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| 0.0615 | 8.0 | 5440 | 0.4615 | 0.8167 | 0.8640 | 0.8397 | 0.9202 | |
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| 0.0468 | 9.0 | 6120 | 0.4922 | 0.8104 | 0.8668 | 0.8376 | 0.9205 | |
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| 0.0381 | 10.0 | 6800 | 0.4991 | 0.8213 | 0.8642 | 0.8422 | 0.9210 | |
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| 0.0381 | 11.0 | 7480 | 0.4950 | 0.8298 | 0.8593 | 0.8443 | 0.9230 | |
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| 0.0289 | 12.0 | 8160 | 0.5273 | 0.8220 | 0.8717 | 0.8461 | 0.9232 | |
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| 0.026 | 13.0 | 8840 | 0.5366 | 0.8207 | 0.8700 | 0.8446 | 0.9230 | |
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| 0.0227 | 14.0 | 9520 | 0.5466 | 0.8240 | 0.8698 | 0.8463 | 0.9230 | |
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| 0.02 | 15.0 | 10200 | 0.5530 | 0.8250 | 0.8710 | 0.8474 | 0.9247 | |
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| 0.0173 | 16.0 | 10880 | 0.5571 | 0.8235 | 0.8726 | 0.8473 | 0.9244 | |
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| 0.0152 | 17.0 | 11560 | 0.5551 | 0.8282 | 0.8700 | 0.8486 | 0.9248 | |
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| 0.0146 | 18.0 | 12240 | 0.5686 | 0.8270 | 0.8727 | 0.8492 | 0.9246 | |
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| 0.0142 | 19.0 | 12920 | 0.5687 | 0.8258 | 0.8753 | 0.8498 | 0.9259 | |
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| 0.0135 | 20.0 | 13600 | 0.5707 | 0.8258 | 0.8753 | 0.8498 | 0.9254 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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