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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: bert-base-uncased |
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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-base-uncased-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-base-uncased-finetuned-ner |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3910 |
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- Precision: 0.9616 |
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- Recall: 0.9637 |
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- F1: 0.9627 |
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- Accuracy: 0.9560 |
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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: 6 |
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- eval_batch_size: 6 |
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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: 15 |
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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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| 0.3052 | 1.0 | 3334 | 0.2630 | 0.9365 | 0.9367 | 0.9366 | 0.9228 | |
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| 0.2104 | 2.0 | 6668 | 0.2481 | 0.9418 | 0.9537 | 0.9477 | 0.9400 | |
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| 0.163 | 3.0 | 10002 | 0.2390 | 0.9495 | 0.9606 | 0.9550 | 0.9479 | |
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| 0.1151 | 4.0 | 13336 | 0.2516 | 0.9549 | 0.9616 | 0.9583 | 0.9515 | |
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| 0.0809 | 5.0 | 16670 | 0.2887 | 0.9590 | 0.9556 | 0.9573 | 0.9493 | |
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| 0.0625 | 6.0 | 20004 | 0.2912 | 0.9573 | 0.9611 | 0.9592 | 0.9520 | |
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| 0.0516 | 7.0 | 23338 | 0.3139 | 0.9581 | 0.9563 | 0.9572 | 0.9501 | |
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| 0.0388 | 8.0 | 26672 | 0.3070 | 0.9605 | 0.9600 | 0.9602 | 0.9531 | |
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| 0.0273 | 9.0 | 30006 | 0.3344 | 0.9607 | 0.9617 | 0.9612 | 0.9535 | |
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| 0.0252 | 10.0 | 33340 | 0.3547 | 0.9608 | 0.9638 | 0.9623 | 0.9554 | |
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| 0.0242 | 11.0 | 36674 | 0.3726 | 0.9600 | 0.9619 | 0.9610 | 0.9541 | |
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| 0.0119 | 12.0 | 40008 | 0.3727 | 0.9602 | 0.9623 | 0.9612 | 0.9546 | |
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| 0.0078 | 13.0 | 43342 | 0.3772 | 0.9617 | 0.9639 | 0.9628 | 0.9562 | |
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| 0.0078 | 14.0 | 46676 | 0.3904 | 0.9615 | 0.9638 | 0.9627 | 0.9560 | |
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| 0.0026 | 15.0 | 50010 | 0.3910 | 0.9616 | 0.9637 | 0.9627 | 0.9560 | |
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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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