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hubert-classifier-aug-ref

Audio Classification
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Safetensors
hubert
Generated from Trainer
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: facebook/hubert-base-ls960
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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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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: hubert-classifier-aug-ref
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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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+ # hubert-classifier-aug-ref
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+
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+ This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.7202
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+ - Accuracy: 0.3221
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+ - Precision: 0.2615
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+ - Recall: 0.3221
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+ - F1: 0.2286
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+ - Binary: 0.5226
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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: 1e-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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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 128
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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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+ - mixed_precision_training: Native AMP
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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 | Precision | Recall | F1 | Binary |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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+ | No log | 0.13 | 50 | 4.4111 | 0.0162 | 0.0031 | 0.0162 | 0.0038 | 0.1228 |
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+ | No log | 0.27 | 100 | 4.3521 | 0.0404 | 0.0228 | 0.0404 | 0.0143 | 0.2263 |
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+ | No log | 0.4 | 150 | 4.2499 | 0.0391 | 0.0038 | 0.0391 | 0.0062 | 0.2493 |
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+ | No log | 0.54 | 200 | 4.1248 | 0.0472 | 0.0045 | 0.0472 | 0.0079 | 0.3098 |
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+ | No log | 0.67 | 250 | 4.0436 | 0.0553 | 0.0057 | 0.0553 | 0.0100 | 0.3318 |
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+ | No log | 0.81 | 300 | 3.9812 | 0.0458 | 0.0028 | 0.0458 | 0.0052 | 0.3237 |
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+ | No log | 0.94 | 350 | 3.9080 | 0.0485 | 0.0045 | 0.0485 | 0.0078 | 0.3279 |
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+ | No log | 1.08 | 400 | 3.8514 | 0.0539 | 0.0063 | 0.0539 | 0.0096 | 0.3344 |
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+ | No log | 1.21 | 450 | 3.7910 | 0.0526 | 0.0059 | 0.0526 | 0.0093 | 0.3330 |
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+ | 4.1459 | 1.35 | 500 | 3.7445 | 0.0539 | 0.0047 | 0.0539 | 0.0084 | 0.3325 |
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+ | 4.1459 | 1.48 | 550 | 3.6971 | 0.0566 | 0.0188 | 0.0566 | 0.0123 | 0.3357 |
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+ | 4.1459 | 1.62 | 600 | 3.6528 | 0.0580 | 0.0075 | 0.0580 | 0.0109 | 0.3371 |
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+ | 4.1459 | 1.75 | 650 | 3.6122 | 0.0593 | 0.0183 | 0.0593 | 0.0146 | 0.3376 |
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+ | 4.1459 | 1.89 | 700 | 3.5826 | 0.0701 | 0.0255 | 0.0701 | 0.0245 | 0.3466 |
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+ | 4.1459 | 2.02 | 750 | 3.5412 | 0.0687 | 0.0159 | 0.0687 | 0.0214 | 0.3464 |
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+ | 4.1459 | 2.16 | 800 | 3.5003 | 0.0714 | 0.0174 | 0.0714 | 0.0236 | 0.3473 |
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+ | 4.1459 | 2.29 | 850 | 3.4698 | 0.0741 | 0.0244 | 0.0741 | 0.0213 | 0.3496 |
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+ | 4.1459 | 2.43 | 900 | 3.4513 | 0.0916 | 0.0342 | 0.0916 | 0.0337 | 0.3637 |
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+ | 4.1459 | 2.56 | 950 | 3.4149 | 0.0863 | 0.0376 | 0.0863 | 0.0336 | 0.3586 |
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+ | 3.6614 | 2.7 | 1000 | 3.3996 | 0.0970 | 0.0413 | 0.0970 | 0.0380 | 0.3668 |
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+ | 3.6614 | 2.83 | 1050 | 3.3726 | 0.0943 | 0.0352 | 0.0943 | 0.0380 | 0.3644 |
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+ | 3.6614 | 2.97 | 1100 | 3.3546 | 0.1146 | 0.0693 | 0.1146 | 0.0574 | 0.3794 |
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+ | 3.6614 | 3.1 | 1150 | 3.3315 | 0.1132 | 0.0649 | 0.1132 | 0.0520 | 0.3806 |
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+ | 3.6614 | 3.24 | 1200 | 3.3058 | 0.1186 | 0.0550 | 0.1186 | 0.0551 | 0.3822 |
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+ | 3.6614 | 3.37 | 1250 | 3.2844 | 0.1199 | 0.0766 | 0.1199 | 0.0592 | 0.3830 |
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+ | 3.6614 | 3.51 | 1300 | 3.2826 | 0.1294 | 0.0661 | 0.1294 | 0.0599 | 0.3881 |
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+ | 3.6614 | 3.64 | 1350 | 3.2405 | 0.1388 | 0.0630 | 0.1388 | 0.0683 | 0.3970 |
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+ | 3.6614 | 3.78 | 1400 | 3.2241 | 0.1563 | 0.0892 | 0.1563 | 0.0810 | 0.4074 |
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+ | 3.6614 | 3.91 | 1450 | 3.2087 | 0.1509 | 0.0765 | 0.1509 | 0.0798 | 0.4053 |
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+ | 3.4148 | 4.05 | 1500 | 3.1872 | 0.1456 | 0.0883 | 0.1456 | 0.0768 | 0.4024 |
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+ | 3.4148 | 4.18 | 1550 | 3.1728 | 0.1577 | 0.1351 | 0.1577 | 0.0899 | 0.4090 |
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+ | 3.4148 | 4.32 | 1600 | 3.1520 | 0.1833 | 0.1670 | 0.1833 | 0.1134 | 0.4270 |
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+ | 3.4148 | 4.45 | 1650 | 3.1339 | 0.1725 | 0.1053 | 0.1725 | 0.1009 | 0.4198 |
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+ | 3.4148 | 4.59 | 1700 | 3.1232 | 0.1698 | 0.1073 | 0.1698 | 0.0979 | 0.4167 |
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+ | 3.4148 | 4.72 | 1750 | 3.1036 | 0.1954 | 0.1416 | 0.1954 | 0.1235 | 0.4350 |
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+ | 3.4148 | 4.86 | 1800 | 3.0891 | 0.1860 | 0.1132 | 0.1860 | 0.1088 | 0.4294 |
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+ | 3.4148 | 4.99 | 1850 | 3.0693 | 0.1927 | 0.1359 | 0.1927 | 0.1167 | 0.4344 |
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+ | 3.4148 | 5.12 | 1900 | 3.0460 | 0.1995 | 0.1617 | 0.1995 | 0.1241 | 0.4379 |
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+ | 3.4148 | 5.26 | 1950 | 3.0348 | 0.2089 | 0.1571 | 0.2089 | 0.1334 | 0.4437 |
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+ | 3.2503 | 5.39 | 2000 | 3.0256 | 0.2170 | 0.1556 | 0.2170 | 0.1357 | 0.4473 |
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+ | 3.2503 | 5.53 | 2050 | 2.9988 | 0.2305 | 0.1749 | 0.2305 | 0.1516 | 0.4600 |
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+ | 3.2503 | 5.66 | 2100 | 2.9890 | 0.2116 | 0.1360 | 0.2116 | 0.1293 | 0.4473 |
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+ | 3.2503 | 5.8 | 2150 | 2.9687 | 0.2345 | 0.1531 | 0.2345 | 0.1483 | 0.4629 |
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+ | 3.2503 | 5.93 | 2200 | 2.9544 | 0.2372 | 0.1516 | 0.2372 | 0.1514 | 0.4652 |
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+ | 3.2503 | 6.07 | 2250 | 2.9427 | 0.2318 | 0.1414 | 0.2318 | 0.1398 | 0.4605 |
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+ | 3.2503 | 6.2 | 2300 | 2.9256 | 0.2453 | 0.1456 | 0.2453 | 0.1539 | 0.4694 |
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+ | 3.2503 | 6.34 | 2350 | 2.9192 | 0.2385 | 0.1552 | 0.2385 | 0.1446 | 0.4652 |
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+ | 3.2503 | 6.47 | 2400 | 2.9028 | 0.2426 | 0.1563 | 0.2426 | 0.1538 | 0.4690 |
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+ | 3.2503 | 6.61 | 2450 | 2.8901 | 0.2480 | 0.1413 | 0.2480 | 0.1552 | 0.4728 |
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+ | 3.1154 | 6.74 | 2500 | 2.8748 | 0.2803 | 0.2016 | 0.2803 | 0.1920 | 0.4954 |
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+ | 3.1154 | 6.88 | 2550 | 2.8688 | 0.2817 | 0.1970 | 0.2817 | 0.1930 | 0.4968 |
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+ | 3.1154 | 7.01 | 2600 | 2.8416 | 0.2830 | 0.1973 | 0.2830 | 0.1917 | 0.4966 |
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+ | 3.1154 | 7.15 | 2650 | 2.8394 | 0.2857 | 0.1908 | 0.2857 | 0.1949 | 0.4987 |
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+ | 3.1154 | 7.28 | 2700 | 2.8327 | 0.2776 | 0.1965 | 0.2776 | 0.1903 | 0.4931 |
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+ | 3.1154 | 7.42 | 2750 | 2.8230 | 0.2736 | 0.1643 | 0.2736 | 0.1808 | 0.4898 |
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+ | 3.1154 | 7.55 | 2800 | 2.8108 | 0.2790 | 0.1886 | 0.2790 | 0.1876 | 0.4930 |
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+ | 3.1154 | 7.69 | 2850 | 2.7987 | 0.2911 | 0.1928 | 0.2911 | 0.1957 | 0.5023 |
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+ | 3.1154 | 7.82 | 2900 | 2.7890 | 0.2965 | 0.2045 | 0.2965 | 0.1999 | 0.5066 |
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+ | 3.1154 | 7.96 | 2950 | 2.7748 | 0.3086 | 0.2352 | 0.3086 | 0.2153 | 0.5140 |
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+ | 3.0145 | 8.09 | 3000 | 2.7694 | 0.3032 | 0.1992 | 0.3032 | 0.2078 | 0.5109 |
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+ | 3.0145 | 8.23 | 3050 | 2.7646 | 0.2992 | 0.2164 | 0.2992 | 0.2077 | 0.5070 |
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+ | 3.0145 | 8.36 | 3100 | 2.7593 | 0.3100 | 0.2394 | 0.3100 | 0.2190 | 0.5160 |
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+ | 3.0145 | 8.5 | 3150 | 2.7552 | 0.3100 | 0.2288 | 0.3100 | 0.2170 | 0.5155 |
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+ | 3.0145 | 8.63 | 3200 | 2.7478 | 0.3181 | 0.2355 | 0.3181 | 0.2241 | 0.5202 |
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+ | 3.0145 | 8.77 | 3250 | 2.7398 | 0.3100 | 0.2264 | 0.3100 | 0.2180 | 0.5146 |
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+ | 3.0145 | 8.9 | 3300 | 2.7403 | 0.3113 | 0.2375 | 0.3113 | 0.2189 | 0.5170 |
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+ | 3.0145 | 9.04 | 3350 | 2.7354 | 0.3073 | 0.2192 | 0.3073 | 0.2143 | 0.5127 |
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+ | 3.0145 | 9.17 | 3400 | 2.7304 | 0.3100 | 0.2260 | 0.3100 | 0.2186 | 0.5146 |
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+ | 3.0145 | 9.31 | 3450 | 2.7282 | 0.3086 | 0.2219 | 0.3086 | 0.2162 | 0.5136 |
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+ | 2.9542 | 9.44 | 3500 | 2.7235 | 0.3167 | 0.2588 | 0.3167 | 0.2248 | 0.5193 |
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+ | 2.9542 | 9.58 | 3550 | 2.7232 | 0.3181 | 0.2408 | 0.3181 | 0.2241 | 0.5202 |
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+ | 2.9542 | 9.71 | 3600 | 2.7217 | 0.3181 | 0.2425 | 0.3181 | 0.2238 | 0.5208 |
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+ | 2.9542 | 9.84 | 3650 | 2.7205 | 0.3194 | 0.2305 | 0.3194 | 0.2235 | 0.5217 |
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+ | 2.9542 | 9.98 | 3700 | 2.7202 | 0.3221 | 0.2615 | 0.3221 | 0.2286 | 0.5226 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.38.2
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+ - Pytorch 2.3.0
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+ - Datasets 2.19.1
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+ - Tokenizers 0.15.1
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