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018-microsoft-MiniLM-finetuned-yahoo-8000_2000

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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: 018-microsoft-MiniLM-finetuned-yahoo-8000_2000
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+ results: []
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+ ---
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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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+
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+ # 018-microsoft-MiniLM-finetuned-yahoo-8000_2000
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+
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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: 1.0511
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+ - F1: 0.6984
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+ - Accuracy: 0.701
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+ - Precision: 0.7000
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+ - Recall: 0.701
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+ - System Ram Used: 4.0180
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+ - System Ram Total: 83.4807
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+ - Gpu Ram Allocated: 0.3995
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+ - Gpu Ram Cached: 12.9297
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+ - Gpu Ram Total: 39.5640
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+ - Gpu Utilization: 35
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+ - Disk Space Used: 26.2045
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+ - Disk Space Total: 78.1898
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 10
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+
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+ ### Training results
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+
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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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+ | 2.1461 | 0.5 | 125 | 1.8487 | 0.4711 | 0.5465 | 0.5181 | 0.5465 | 3.8798 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 28 | 24.5841 | 78.1898 |
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+ | 1.6793 | 1.0 | 250 | 1.5280 | 0.5799 | 0.615 | 0.6207 | 0.615 | 3.8827 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 28 | 24.5842 | 78.1898 |
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+ | 1.4163 | 1.5 | 375 | 1.3396 | 0.6508 | 0.6675 | 0.6691 | 0.6675 | 3.8831 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 28 | 24.5842 | 78.1898 |
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+ | 1.2855 | 2.0 | 500 | 1.2413 | 0.6633 | 0.6745 | 0.6742 | 0.6745 | 3.8975 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 30 | 24.5843 | 78.1898 |
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+ | 1.1364 | 2.5 | 625 | 1.1795 | 0.6658 | 0.6725 | 0.6758 | 0.6725 | 4.0967 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 31 | 25.4571 | 78.1898 |
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+ | 1.0569 | 3.0 | 750 | 1.1167 | 0.6785 | 0.6845 | 0.6841 | 0.6845 | 4.0923 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 29 | 25.4573 | 78.1898 |
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+ | 0.9596 | 3.5 | 875 | 1.0866 | 0.6883 | 0.698 | 0.6920 | 0.698 | 3.8765 | 83.4807 | 0.3997 | 12.9297 | 39.5640 | 29 | 25.4573 | 78.1898 |
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+ | 0.917 | 4.0 | 1000 | 1.0703 | 0.6796 | 0.6875 | 0.6841 | 0.6875 | 3.8976 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 29 | 25.4573 | 78.1898 |
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+ | 0.8512 | 4.5 | 1125 | 1.0629 | 0.6913 | 0.6915 | 0.6945 | 0.6915 | 4.0600 | 83.4807 | 0.3997 | 12.9297 | 39.5640 | 28 | 25.8306 | 78.1898 |
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+ | 0.8121 | 5.0 | 1250 | 1.0576 | 0.6838 | 0.691 | 0.6905 | 0.691 | 4.0432 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 31 | 25.8306 | 78.1898 |
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+ | 0.7733 | 5.5 | 1375 | 1.0598 | 0.6774 | 0.6805 | 0.6838 | 0.6805 | 3.8379 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 28 | 25.8307 | 78.1898 |
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+ | 0.7431 | 6.0 | 1500 | 1.0376 | 0.6974 | 0.702 | 0.6976 | 0.702 | 3.8546 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 31 | 25.8307 | 78.1898 |
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+ | 0.7065 | 6.5 | 1625 | 1.0457 | 0.6990 | 0.6995 | 0.7014 | 0.6995 | 4.0339 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 28 | 26.2040 | 78.1898 |
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+ | 0.671 | 7.0 | 1750 | 1.0396 | 0.6956 | 0.698 | 0.6966 | 0.698 | 4.0384 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 28 | 26.2040 | 78.1898 |
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+ | 0.6438 | 7.5 | 1875 | 1.0474 | 0.6887 | 0.6925 | 0.6907 | 0.6925 | 3.8274 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 28 | 26.2040 | 78.1898 |
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+ | 0.6326 | 8.0 | 2000 | 1.0384 | 0.6972 | 0.698 | 0.6983 | 0.698 | 3.8402 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 34 | 26.2041 | 78.1898 |
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+ | 0.6121 | 8.5 | 2125 | 1.0440 | 0.6963 | 0.698 | 0.6976 | 0.698 | 4.0162 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 29 | 26.2042 | 78.1898 |
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+ | 0.5911 | 9.0 | 2250 | 1.0518 | 0.6995 | 0.701 | 0.7006 | 0.701 | 4.0338 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 28 | 26.2043 | 78.1898 |
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+ | 0.592 | 9.5 | 2375 | 1.0490 | 0.7023 | 0.7035 | 0.7025 | 0.7035 | 3.8126 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 27 | 26.2043 | 78.1898 |
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+ | 0.5586 | 10.0 | 2500 | 1.0511 | 0.6984 | 0.701 | 0.7000 | 0.701 | 3.8448 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 27 | 26.2043 | 78.1898 |
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+
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+
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+ ### Framework versions
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+
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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