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TUF_ALBERT_5E

Text Classification
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albert
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: TUF_ALBERT_5E
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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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+ # TUF_ALBERT_5E
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+
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+ This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2389
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+ - Accuracy: 0.9533
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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: 3e-05
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+ - train_batch_size: 16
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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: 5
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.5099 | 0.1 | 50 | 0.3861 | 0.8533 |
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+ | 0.2985 | 0.2 | 100 | 0.2961 | 0.8933 |
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+ | 0.2972 | 0.3 | 150 | 0.2335 | 0.9333 |
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+ | 0.2835 | 0.4 | 200 | 0.1872 | 0.94 |
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+ | 0.26 | 0.5 | 250 | 0.4147 | 0.9133 |
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+ | 0.2986 | 0.59 | 300 | 0.2080 | 0.9267 |
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+ | 0.2554 | 0.69 | 350 | 0.3984 | 0.9133 |
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+ | 0.2306 | 0.79 | 400 | 0.2136 | 0.9333 |
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+ | 0.2218 | 0.89 | 450 | 0.4455 | 0.8867 |
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+ | 0.2113 | 0.99 | 500 | 0.2205 | 0.94 |
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+ | 0.2541 | 1.09 | 550 | 0.1705 | 0.9333 |
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+ | 0.1947 | 1.19 | 600 | 0.3264 | 0.8933 |
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+ | 0.2409 | 1.29 | 650 | 0.2084 | 0.92 |
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+ | 0.1968 | 1.39 | 700 | 0.2550 | 0.9267 |
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+ | 0.172 | 1.49 | 750 | 0.2238 | 0.9467 |
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+ | 0.1478 | 1.58 | 800 | 0.2501 | 0.9533 |
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+ | 0.2199 | 1.68 | 850 | 0.2618 | 0.9133 |
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+ | 0.1792 | 1.78 | 900 | 0.2109 | 0.9267 |
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+ | 0.1831 | 1.88 | 950 | 0.2641 | 0.92 |
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+ | 0.1534 | 1.98 | 1000 | 0.1924 | 0.94 |
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+ | 0.1208 | 2.08 | 1050 | 0.2990 | 0.9333 |
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+ | 0.1118 | 2.18 | 1100 | 0.4952 | 0.9 |
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+ | 0.158 | 2.28 | 1150 | 0.1706 | 0.9533 |
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+ | 0.1163 | 2.38 | 1200 | 0.1238 | 0.9733 |
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+ | 0.1738 | 2.48 | 1250 | 0.1989 | 0.9467 |
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+ | 0.1305 | 2.57 | 1300 | 0.4354 | 0.9067 |
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+ | 0.1668 | 2.67 | 1350 | 0.1276 | 0.9667 |
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+ | 0.1195 | 2.77 | 1400 | 0.2170 | 0.9533 |
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+ | 0.1057 | 2.87 | 1450 | 0.2882 | 0.9333 |
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+ | 0.1172 | 2.97 | 1500 | 0.1435 | 0.9667 |
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+ | 0.0893 | 3.07 | 1550 | 0.1754 | 0.96 |
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+ | 0.0582 | 3.17 | 1600 | 0.1858 | 0.96 |
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+ | 0.0887 | 3.27 | 1650 | 0.4954 | 0.92 |
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+ | 0.1166 | 3.37 | 1700 | 0.2356 | 0.9467 |
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+ | 0.0518 | 3.47 | 1750 | 0.1910 | 0.96 |
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+ | 0.0741 | 3.56 | 1800 | 0.1328 | 0.9733 |
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+ | 0.072 | 3.66 | 1850 | 0.2769 | 0.9467 |
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+ | 0.0534 | 3.76 | 1900 | 0.3501 | 0.94 |
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+ | 0.0776 | 3.86 | 1950 | 0.3171 | 0.94 |
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+ | 0.0537 | 3.96 | 2000 | 0.2138 | 0.9533 |
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+ | 0.0683 | 4.06 | 2050 | 0.2934 | 0.94 |
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+ | 0.015 | 4.16 | 2100 | 0.2233 | 0.9533 |
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+ | 0.0236 | 4.26 | 2150 | 0.2673 | 0.9533 |
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+ | 0.0357 | 4.36 | 2200 | 0.2279 | 0.96 |
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+ | 0.0298 | 4.46 | 2250 | 0.3017 | 0.9467 |
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+ | 0.0357 | 4.55 | 2300 | 0.2910 | 0.9467 |
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+ | 0.0208 | 4.65 | 2350 | 0.2498 | 0.9533 |
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+ | 0.0345 | 4.75 | 2400 | 0.2259 | 0.9667 |
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+ | 0.0174 | 4.85 | 2450 | 0.2274 | 0.9667 |
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+ | 0.0393 | 4.95 | 2500 | 0.2389 | 0.9533 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.24.0
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+ - Pytorch 1.13.0
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+ - Datasets 2.7.1
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+ - Tokenizers 0.13.2