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--- |
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license: mit |
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base_model: microsoft/MiniLM-L12-H384-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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- precision |
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- recall |
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model-index: |
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- name: 017-microsoft-MiniLM-finetuned-yahoo-800_200 |
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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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# 017-microsoft-MiniLM-finetuned-yahoo-800_200 |
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This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4048 |
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- F1: 0.6237 |
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- Accuracy: 0.63 |
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- Precision: 0.6273 |
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- Recall: 0.63 |
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- System Ram Used: 3.8778 |
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- System Ram Total: 83.4807 |
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- Gpu Ram Allocated: 0.3903 |
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- Gpu Ram Cached: 12.8340 |
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- Gpu Ram Total: 39.5640 |
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- Gpu Utilization: 32 |
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- Disk Space Used: 25.4337 |
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- Disk Space Total: 78.1898 |
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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: 32 |
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- eval_batch_size: 32 |
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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: 25 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall | System Ram Used | System Ram Total | Gpu Ram Allocated | Gpu Ram Cached | Gpu Ram Total | Gpu Utilization | Disk Space Used | Disk Space Total | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|:---------------:|:----------------:|:-----------------:|:--------------:|:-------------:|:---------------:|:---------------:|:----------------:| |
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| 2.3021 | 1.28 | 32 | 2.2975 | 0.0519 | 0.12 | 0.1102 | 0.12 | 3.8424 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 29 | 24.5606 | 78.1898 | |
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| 2.2615 | 2.56 | 64 | 2.1926 | 0.2339 | 0.31 | 0.4649 | 0.31 | 3.8514 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 30 | 24.5606 | 78.1898 | |
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| 2.0677 | 3.84 | 96 | 1.9658 | 0.4301 | 0.51 | 0.3950 | 0.51 | 3.8537 | 83.4807 | 0.3905 | 12.8340 | 39.5640 | 22 | 24.5606 | 78.1898 | |
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| 1.8562 | 5.12 | 128 | 1.8383 | 0.4655 | 0.545 | 0.4587 | 0.545 | 3.8574 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 41 | 24.5606 | 78.1898 | |
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| 1.6929 | 6.4 | 160 | 1.7403 | 0.4942 | 0.555 | 0.5261 | 0.555 | 3.8549 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 29 | 24.5607 | 78.1898 | |
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| 1.5569 | 7.68 | 192 | 1.6663 | 0.5467 | 0.585 | 0.6496 | 0.585 | 3.8549 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 37 | 24.5607 | 78.1898 | |
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| 1.4636 | 8.96 | 224 | 1.6123 | 0.5475 | 0.58 | 0.5539 | 0.58 | 3.8539 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 30 | 24.5607 | 78.1898 | |
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| 1.3683 | 10.24 | 256 | 1.5615 | 0.5829 | 0.595 | 0.6016 | 0.595 | 3.8527 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 41 | 24.5607 | 78.1898 | |
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| 1.2649 | 11.52 | 288 | 1.5261 | 0.5904 | 0.61 | 0.6243 | 0.61 | 3.8646 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 30 | 24.5607 | 78.1898 | |
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| 1.1968 | 12.8 | 320 | 1.4976 | 0.6012 | 0.615 | 0.6070 | 0.615 | 3.8766 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 45 | 24.5607 | 78.1898 | |
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| 1.1291 | 14.08 | 352 | 1.4756 | 0.5983 | 0.615 | 0.6164 | 0.615 | 3.8749 | 83.4807 | 0.3905 | 12.8340 | 39.5640 | 47 | 24.5607 | 78.1898 | |
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| 1.0673 | 15.36 | 384 | 1.4660 | 0.6064 | 0.62 | 0.6258 | 0.62 | 3.8752 | 83.4807 | 0.3907 | 12.8340 | 39.5640 | 35 | 24.5607 | 78.1898 | |
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| 0.9884 | 16.64 | 416 | 1.4410 | 0.6135 | 0.625 | 0.6204 | 0.625 | 3.8757 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 33 | 24.5608 | 78.1898 | |
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| 0.9743 | 17.92 | 448 | 1.4328 | 0.6233 | 0.635 | 0.6343 | 0.635 | 3.8747 | 83.4807 | 0.3905 | 12.8340 | 39.5640 | 44 | 24.5608 | 78.1898 | |
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| 0.926 | 19.2 | 480 | 1.4344 | 0.6088 | 0.615 | 0.6238 | 0.615 | 3.8742 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 31 | 24.5608 | 78.1898 | |
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| 0.8815 | 20.48 | 512 | 1.4282 | 0.6235 | 0.625 | 0.6350 | 0.625 | 4.0591 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 43 | 25.4337 | 78.1898 | |
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| 0.8613 | 21.76 | 544 | 1.4146 | 0.6329 | 0.635 | 0.6408 | 0.635 | 4.0655 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 26 | 25.4337 | 78.1898 | |
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| 0.8466 | 23.04 | 576 | 1.4086 | 0.6318 | 0.635 | 0.6415 | 0.635 | 4.0544 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 35 | 25.4337 | 78.1898 | |
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| 0.8282 | 24.32 | 608 | 1.4058 | 0.6243 | 0.63 | 0.6319 | 0.63 | 3.8886 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 27 | 25.4337 | 78.1898 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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