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--- |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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datasets: |
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- adalbertojunior/segmentacao |
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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: test_v7 |
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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: adalbertojunior/segmentacao |
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type: adalbertojunior/segmentacao |
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config: segmentacao |
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split: validation |
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args: segmentacao |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.6657754010695187 |
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- name: Recall |
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type: recall |
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value: 0.6859504132231405 |
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- name: F1 |
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type: f1 |
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value: 0.6757123473541385 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9990518084066471 |
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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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# test_v7 |
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This model is a fine-tuned version of [./models/distill-bge-retromae-step](https://huggingface.co/./models/distill-bge-retromae-step) on the adalbertojunior/segmentacao dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0045 |
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- Precision: 0.6658 |
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- Recall: 0.6860 |
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- F1: 0.6757 |
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- Accuracy: 0.9991 |
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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: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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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: 3.0 |
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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 | 0.0637 | 100 | 0.0048 | 0.5339 | 0.5647 | 0.5489 | 0.9984 | |
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| No log | 0.1274 | 200 | 0.0048 | 0.5567 | 0.6226 | 0.5878 | 0.9987 | |
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| No log | 0.1911 | 300 | 0.0048 | 0.5745 | 0.5950 | 0.5846 | 0.9988 | |
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| No log | 0.2548 | 400 | 0.0048 | 0.5622 | 0.5978 | 0.5794 | 0.9988 | |
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| 0.0061 | 0.3185 | 500 | 0.0069 | 0.48 | 0.5950 | 0.5314 | 0.9983 | |
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| 0.0061 | 0.3822 | 600 | 0.0061 | 0.5692 | 0.6116 | 0.5896 | 0.9987 | |
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| 0.0061 | 0.4459 | 700 | 0.0052 | 0.5736 | 0.6226 | 0.5971 | 0.9988 | |
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| 0.0061 | 0.5096 | 800 | 0.0055 | 0.5921 | 0.6198 | 0.6057 | 0.9988 | |
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| 0.0061 | 0.5733 | 900 | 0.0057 | 0.6126 | 0.6446 | 0.6282 | 0.9989 | |
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| 0.0008 | 0.6370 | 1000 | 0.0065 | 0.5635 | 0.6116 | 0.5865 | 0.9987 | |
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| 0.0008 | 0.7007 | 1100 | 0.0060 | 0.5725 | 0.6529 | 0.6100 | 0.9987 | |
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| 0.0008 | 0.7645 | 1200 | 0.0061 | 0.5704 | 0.6474 | 0.6065 | 0.9988 | |
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| 0.0008 | 0.8282 | 1300 | 0.0053 | 0.5813 | 0.6501 | 0.6138 | 0.9988 | |
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| 0.0008 | 0.8919 | 1400 | 0.0045 | 0.6658 | 0.6860 | 0.6757 | 0.9991 | |
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| 0.0004 | 0.9556 | 1500 | 0.0049 | 0.6497 | 0.6694 | 0.6594 | 0.9990 | |
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| 0.0004 | 1.0193 | 1600 | 0.0054 | 0.5707 | 0.6446 | 0.6054 | 0.9988 | |
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| 0.0004 | 1.0830 | 1700 | 0.0047 | 0.6376 | 0.6639 | 0.6505 | 0.9990 | |
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| 0.0004 | 1.1467 | 1800 | 0.0048 | 0.5922 | 0.6722 | 0.6297 | 0.9989 | |
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| 0.0004 | 1.2104 | 1900 | 0.0041 | 0.6455 | 0.6722 | 0.6586 | 0.9990 | |
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| 0.0002 | 1.2741 | 2000 | 0.0053 | 0.5686 | 0.6391 | 0.6018 | 0.9987 | |
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| 0.0002 | 1.3378 | 2100 | 0.0046 | 0.6495 | 0.6942 | 0.6711 | 0.9990 | |
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| 0.0002 | 1.4015 | 2200 | 0.0049 | 0.5947 | 0.6749 | 0.6323 | 0.9988 | |
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| 0.0002 | 1.4652 | 2300 | 0.0045 | 0.6125 | 0.6749 | 0.6422 | 0.9989 | |
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| 0.0002 | 1.5289 | 2400 | 0.0045 | 0.5701 | 0.6722 | 0.6169 | 0.9988 | |
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| 0.0002 | 1.5926 | 2500 | 0.0058 | 0.5321 | 0.6391 | 0.5807 | 0.9986 | |
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| 0.0002 | 1.6563 | 2600 | 0.0056 | 0.5110 | 0.6419 | 0.5690 | 0.9985 | |
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| 0.0002 | 1.7200 | 2700 | 0.0052 | 0.5792 | 0.6446 | 0.6102 | 0.9988 | |
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| 0.0002 | 1.7837 | 2800 | 0.0047 | 0.5941 | 0.6612 | 0.6258 | 0.9989 | |
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| 0.0002 | 1.8474 | 2900 | 0.0051 | 0.5655 | 0.6419 | 0.6013 | 0.9988 | |
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| 0.0001 | 1.9111 | 3000 | 0.0044 | 0.5866 | 0.6529 | 0.6180 | 0.9989 | |
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| 0.0001 | 1.9748 | 3100 | 0.0042 | 0.5792 | 0.6446 | 0.6102 | 0.9988 | |
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| 0.0001 | 2.0385 | 3200 | 0.0045 | 0.6015 | 0.6694 | 0.6336 | 0.9989 | |
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| 0.0001 | 2.1022 | 3300 | 0.0063 | 0.5409 | 0.6556 | 0.5928 | 0.9987 | |
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| 0.0001 | 2.1659 | 3400 | 0.0047 | 0.5887 | 0.6584 | 0.6216 | 0.9989 | |
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| 0.0001 | 2.2297 | 3500 | 0.0045 | 0.6131 | 0.6722 | 0.6413 | 0.9989 | |
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| 0.0001 | 2.2934 | 3600 | 0.0047 | 0.6193 | 0.6722 | 0.6446 | 0.9989 | |
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| 0.0001 | 2.3571 | 3700 | 0.0047 | 0.6091 | 0.6612 | 0.6341 | 0.9989 | |
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| 0.0001 | 2.4208 | 3800 | 0.0047 | 0.6205 | 0.6667 | 0.6428 | 0.9989 | |
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| 0.0001 | 2.4845 | 3900 | 0.0044 | 0.6070 | 0.6722 | 0.6379 | 0.9989 | |
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| 0.0001 | 2.5482 | 4000 | 0.0052 | 0.5355 | 0.6226 | 0.5758 | 0.9987 | |
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| 0.0001 | 2.6119 | 4100 | 0.0047 | 0.5871 | 0.6501 | 0.6170 | 0.9989 | |
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| 0.0001 | 2.6756 | 4200 | 0.0049 | 0.5739 | 0.6419 | 0.6060 | 0.9988 | |
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| 0.0001 | 2.7393 | 4300 | 0.0049 | 0.5634 | 0.6364 | 0.5977 | 0.9988 | |
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| 0.0001 | 2.8030 | 4400 | 0.0052 | 0.5634 | 0.6364 | 0.5977 | 0.9988 | |
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| 0.0 | 2.8667 | 4500 | 0.0049 | 0.5739 | 0.6419 | 0.6060 | 0.9988 | |
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| 0.0 | 2.9304 | 4600 | 0.0044 | 0.5796 | 0.6419 | 0.6092 | 0.9988 | |
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| 0.0 | 2.9941 | 4700 | 0.0047 | 0.5796 | 0.6419 | 0.6092 | 0.9988 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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