End of training
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README.md
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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: 0.
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- Accuracy: 0.
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- Precision: 0.
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- Recall: 0.
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- F1: 0.
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- Binary: 0.
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## Model description
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@@ -53,116 +53,102 @@ The following hyperparameters were used during training:
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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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- lr_scheduler_warmup_steps: 500
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- num_epochs:
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- mixed_precision_training: Native AMP
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### Training results
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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 | 0.24 | 50 | 4.
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| No log | 0.48 | 100 | 4.
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| No log | 0.72 | 150 | 3.
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| No log | 0.96 | 200 | 3.
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| 0.3685 | 21.56 | 4500 | 0.5753 | 0.8974 | 0.9047 | 0.8974 | 0.8966 | 0.9285 |
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| 153 |
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| 0.3685 | 21.8 | 4550 | 0.5693 | 0.8891 | 0.8959 | 0.8891 | 0.8873 | 0.9227 |
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| 154 |
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| 0.3472 | 22.04 | 4600 | 0.5866 | 0.8831 | 0.8905 | 0.8831 | 0.8820 | 0.9184 |
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| 155 |
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| 0.3472 | 22.28 | 4650 | 0.5781 | 0.8899 | 0.8969 | 0.8899 | 0.8892 | 0.9233 |
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| 156 |
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| 0.3472 | 22.51 | 4700 | 0.6050 | 0.8921 | 0.8989 | 0.8921 | 0.8910 | 0.9240 |
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| 157 |
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| 0.3472 | 22.75 | 4750 | 0.5826 | 0.8914 | 0.8965 | 0.8914 | 0.8906 | 0.9238 |
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| 158 |
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| 0.3472 | 22.99 | 4800 | 0.5809 | 0.8981 | 0.9050 | 0.8981 | 0.8973 | 0.9285 |
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| 159 |
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| 0.3316 | 23.23 | 4850 | 0.6249 | 0.8869 | 0.8942 | 0.8869 | 0.8865 | 0.9210 |
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| 160 |
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| 0.3316 | 23.47 | 4900 | 0.5971 | 0.8876 | 0.8937 | 0.8876 | 0.8869 | 0.9214 |
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| 161 |
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| 0.3316 | 23.71 | 4950 | 0.5849 | 0.8884 | 0.8948 | 0.8884 | 0.8883 | 0.9217 |
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| 162 |
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| 0.3316 | 23.95 | 5000 | 0.5806 | 0.8854 | 0.8913 | 0.8854 | 0.8854 | 0.9199 |
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| 163 |
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| 0.3066 | 24.19 | 5050 | 0.5833 | 0.8936 | 0.8996 | 0.8936 | 0.8929 | 0.9254 |
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| 164 |
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| 0.3066 | 24.43 | 5100 | 0.5802 | 0.8966 | 0.9033 | 0.8966 | 0.8963 | 0.9275 |
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| 165 |
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| 0.3066 | 24.67 | 5150 | 0.5742 | 0.8906 | 0.8971 | 0.8906 | 0.8901 | 0.9233 |
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### Framework versions
|
|
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|
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| 21 |
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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| 22 |
It achieves the following results on the evaluation set:
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+
- Loss: 0.6784
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+
- Accuracy: 0.8612
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+
- Precision: 0.8732
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+
- Recall: 0.8612
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- F1: 0.8586
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| 28 |
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- Binary: 0.9035
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| 29 |
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## Model description
|
| 31 |
|
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| 53 |
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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| 54 |
- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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| 56 |
+
- num_epochs: 100
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| 57 |
- mixed_precision_training: Native AMP
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### Training results
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| 60 |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
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| 62 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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| 63 |
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| No log | 0.24 | 50 | 4.4161 | 0.0180 | 0.0182 | 0.0180 | 0.0106 | 0.1504 |
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| 64 |
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| No log | 0.48 | 100 | 4.2802 | 0.0375 | 0.0091 | 0.0375 | 0.0074 | 0.3082 |
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| 65 |
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| No log | 0.72 | 150 | 3.9834 | 0.0502 | 0.0086 | 0.0502 | 0.0109 | 0.3226 |
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| 66 |
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| No log | 0.96 | 200 | 3.7286 | 0.0569 | 0.0236 | 0.0569 | 0.0159 | 0.3276 |
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| 4.2271 | 1.2 | 250 | 3.4426 | 0.0891 | 0.0270 | 0.0891 | 0.0322 | 0.3596 |
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| 4.2271 | 1.44 | 300 | 3.2540 | 0.1169 | 0.0601 | 0.1169 | 0.0604 | 0.3788 |
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| 4.2271 | 1.68 | 350 | 3.1869 | 0.1176 | 0.0721 | 0.1176 | 0.0598 | 0.3683 |
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| 4.2271 | 1.92 | 400 | 2.8711 | 0.1618 | 0.1145 | 0.1618 | 0.0984 | 0.4101 |
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| 3.3668 | 2.16 | 450 | 2.6606 | 0.2644 | 0.1518 | 0.2644 | 0.1626 | 0.4816 |
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| 3.3668 | 2.4 | 500 | 2.3190 | 0.3670 | 0.2659 | 0.3670 | 0.2721 | 0.5538 |
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| 3.3668 | 2.63 | 550 | 2.0561 | 0.4120 | 0.3507 | 0.4120 | 0.3239 | 0.5857 |
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| 74 |
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| 3.3668 | 2.87 | 600 | 1.8485 | 0.4764 | 0.4155 | 0.4764 | 0.4052 | 0.6330 |
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| 2.5092 | 3.11 | 650 | 1.7040 | 0.5296 | 0.4975 | 0.5296 | 0.4731 | 0.6697 |
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| 2.5092 | 3.35 | 700 | 1.4804 | 0.5970 | 0.5614 | 0.5970 | 0.5443 | 0.7167 |
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| 2.5092 | 3.59 | 750 | 1.3268 | 0.6434 | 0.6271 | 0.6434 | 0.6047 | 0.7488 |
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| 78 |
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| 2.5092 | 3.83 | 800 | 1.2244 | 0.6749 | 0.6423 | 0.6749 | 0.6342 | 0.7728 |
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| 79 |
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| 1.771 | 4.07 | 850 | 1.0787 | 0.7348 | 0.7587 | 0.7348 | 0.7168 | 0.8142 |
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| 80 |
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| 1.771 | 4.31 | 900 | 1.0527 | 0.7281 | 0.7380 | 0.7281 | 0.7070 | 0.8097 |
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| 81 |
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| 1.771 | 4.55 | 950 | 0.9342 | 0.7596 | 0.7759 | 0.7596 | 0.7454 | 0.8314 |
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| 82 |
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| 1.771 | 4.79 | 1000 | 0.8399 | 0.7880 | 0.7986 | 0.7880 | 0.7766 | 0.8507 |
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| 83 |
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| 1.3767 | 5.03 | 1050 | 0.8286 | 0.7970 | 0.8035 | 0.7970 | 0.7883 | 0.8575 |
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| 84 |
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| 1.3767 | 5.27 | 1100 | 0.8207 | 0.7888 | 0.8016 | 0.7888 | 0.7823 | 0.8524 |
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| 85 |
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| 1.3767 | 5.51 | 1150 | 0.7596 | 0.8112 | 0.8180 | 0.8112 | 0.8033 | 0.8690 |
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| 86 |
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| 1.3767 | 5.75 | 1200 | 0.7087 | 0.8067 | 0.8139 | 0.8067 | 0.8007 | 0.8658 |
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| 87 |
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| 1.3767 | 5.99 | 1250 | 0.7088 | 0.8045 | 0.8178 | 0.8045 | 0.7991 | 0.8637 |
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| 88 |
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| 1.1079 | 6.23 | 1300 | 0.7062 | 0.8150 | 0.8256 | 0.8150 | 0.8101 | 0.8698 |
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| 89 |
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| 1.1079 | 6.47 | 1350 | 0.6382 | 0.8285 | 0.8385 | 0.8285 | 0.8272 | 0.8810 |
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| 90 |
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| 1.1079 | 6.71 | 1400 | 0.6746 | 0.8240 | 0.8386 | 0.8240 | 0.8209 | 0.8783 |
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| 91 |
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| 1.1079 | 6.95 | 1450 | 0.6312 | 0.8367 | 0.8523 | 0.8367 | 0.8347 | 0.8867 |
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| 92 |
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| 0.9652 | 7.19 | 1500 | 0.6707 | 0.8255 | 0.8438 | 0.8255 | 0.8215 | 0.8775 |
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| 93 |
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| 0.9652 | 7.43 | 1550 | 0.6126 | 0.8479 | 0.8578 | 0.8479 | 0.8449 | 0.8942 |
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| 94 |
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| 0.9652 | 7.66 | 1600 | 0.6500 | 0.8427 | 0.8528 | 0.8427 | 0.8397 | 0.8912 |
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| 0.9652 | 7.9 | 1650 | 0.6272 | 0.8412 | 0.8512 | 0.8412 | 0.8375 | 0.8885 |
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| 0.8436 | 8.14 | 1700 | 0.6499 | 0.8509 | 0.8630 | 0.8509 | 0.8470 | 0.8970 |
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| 0.8436 | 8.38 | 1750 | 0.6836 | 0.8337 | 0.8423 | 0.8337 | 0.8294 | 0.8841 |
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| 0.8436 | 8.62 | 1800 | 0.6261 | 0.8487 | 0.8614 | 0.8487 | 0.8478 | 0.8951 |
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| 0.8436 | 8.86 | 1850 | 0.5969 | 0.8584 | 0.8631 | 0.8584 | 0.8555 | 0.9019 |
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| 0.7658 | 9.1 | 1900 | 0.6646 | 0.8397 | 0.8561 | 0.8397 | 0.8357 | 0.8872 |
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| 0.7658 | 9.34 | 1950 | 0.5753 | 0.8644 | 0.8715 | 0.8644 | 0.8624 | 0.9049 |
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| 102 |
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| 0.7658 | 9.58 | 2000 | 0.6675 | 0.8404 | 0.8511 | 0.8404 | 0.8365 | 0.8885 |
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| 103 |
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| 0.7658 | 9.82 | 2050 | 0.6864 | 0.8360 | 0.8479 | 0.8360 | 0.8319 | 0.8859 |
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| 104 |
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| 0.6854 | 10.06 | 2100 | 0.6580 | 0.8479 | 0.8599 | 0.8479 | 0.8435 | 0.8948 |
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| 105 |
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| 0.6854 | 10.3 | 2150 | 0.6755 | 0.8509 | 0.8627 | 0.8509 | 0.8487 | 0.8963 |
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| 106 |
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| 0.6854 | 10.54 | 2200 | 0.6949 | 0.8524 | 0.8625 | 0.8524 | 0.8499 | 0.8969 |
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| 107 |
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| 0.6854 | 10.78 | 2250 | 0.7240 | 0.8434 | 0.8511 | 0.8434 | 0.8411 | 0.8905 |
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| 108 |
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| 0.6444 | 11.02 | 2300 | 0.6266 | 0.8502 | 0.8607 | 0.8502 | 0.8462 | 0.8950 |
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| 109 |
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| 0.6444 | 11.26 | 2350 | 0.6061 | 0.8674 | 0.8795 | 0.8674 | 0.8647 | 0.9073 |
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| 110 |
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| 0.6444 | 11.5 | 2400 | 0.6550 | 0.8509 | 0.8616 | 0.8509 | 0.8477 | 0.8955 |
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| 111 |
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| 0.6444 | 11.74 | 2450 | 0.6460 | 0.8457 | 0.8553 | 0.8457 | 0.8441 | 0.8913 |
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| 112 |
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| 0.6444 | 11.98 | 2500 | 0.5699 | 0.8577 | 0.8679 | 0.8577 | 0.8572 | 0.9010 |
|
| 113 |
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| 0.6038 | 12.22 | 2550 | 0.6236 | 0.8517 | 0.8576 | 0.8517 | 0.8491 | 0.8963 |
|
| 114 |
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| 0.6038 | 12.46 | 2600 | 0.5718 | 0.8674 | 0.8766 | 0.8674 | 0.8639 | 0.9071 |
|
| 115 |
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| 0.6038 | 12.69 | 2650 | 0.5904 | 0.8644 | 0.8753 | 0.8644 | 0.8649 | 0.9061 |
|
| 116 |
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| 0.6038 | 12.93 | 2700 | 0.6894 | 0.8487 | 0.8614 | 0.8487 | 0.8470 | 0.8951 |
|
| 117 |
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| 0.5691 | 13.17 | 2750 | 0.6029 | 0.8652 | 0.8777 | 0.8652 | 0.8643 | 0.9064 |
|
| 118 |
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| 0.5691 | 13.41 | 2800 | 0.6195 | 0.8727 | 0.8842 | 0.8727 | 0.8721 | 0.9105 |
|
| 119 |
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| 0.5691 | 13.65 | 2850 | 0.6300 | 0.8682 | 0.8776 | 0.8682 | 0.8668 | 0.9076 |
|
| 120 |
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| 0.5691 | 13.89 | 2900 | 0.6413 | 0.8644 | 0.8729 | 0.8644 | 0.8618 | 0.9058 |
|
| 121 |
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| 0.5315 | 14.13 | 2950 | 0.7475 | 0.8509 | 0.8632 | 0.8509 | 0.8477 | 0.8958 |
|
| 122 |
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| 0.5315 | 14.37 | 3000 | 0.6623 | 0.8659 | 0.8756 | 0.8659 | 0.8641 | 0.9069 |
|
| 123 |
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| 0.5315 | 14.61 | 3050 | 0.6826 | 0.8547 | 0.8643 | 0.8547 | 0.8522 | 0.8978 |
|
| 124 |
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| 0.5315 | 14.85 | 3100 | 0.6302 | 0.8712 | 0.8797 | 0.8712 | 0.8694 | 0.9097 |
|
| 125 |
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| 0.5031 | 15.09 | 3150 | 0.5901 | 0.8787 | 0.8846 | 0.8787 | 0.8769 | 0.9157 |
|
| 126 |
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| 0.5031 | 15.33 | 3200 | 0.6089 | 0.8652 | 0.8746 | 0.8652 | 0.8632 | 0.9056 |
|
| 127 |
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| 0.5031 | 15.57 | 3250 | 0.6068 | 0.8719 | 0.8783 | 0.8719 | 0.8708 | 0.9108 |
|
| 128 |
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| 0.5031 | 15.81 | 3300 | 0.6462 | 0.8652 | 0.8738 | 0.8652 | 0.8632 | 0.9056 |
|
| 129 |
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| 0.4759 | 16.05 | 3350 | 0.6459 | 0.8607 | 0.8718 | 0.8607 | 0.8591 | 0.9013 |
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| 130 |
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| 0.4759 | 16.29 | 3400 | 0.6432 | 0.8644 | 0.8741 | 0.8644 | 0.8629 | 0.9052 |
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| 131 |
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| 0.4759 | 16.53 | 3450 | 0.6266 | 0.8652 | 0.8731 | 0.8652 | 0.8640 | 0.9058 |
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| 132 |
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| 0.4759 | 16.77 | 3500 | 0.5806 | 0.8824 | 0.8904 | 0.8824 | 0.8823 | 0.9170 |
|
| 133 |
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| 0.4731 | 17.01 | 3550 | 0.6293 | 0.8697 | 0.8792 | 0.8697 | 0.8698 | 0.9089 |
|
| 134 |
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| 0.4731 | 17.25 | 3600 | 0.6389 | 0.8682 | 0.8786 | 0.8682 | 0.8681 | 0.9079 |
|
| 135 |
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| 0.4731 | 17.49 | 3650 | 0.6320 | 0.8712 | 0.8773 | 0.8712 | 0.8696 | 0.9098 |
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| 136 |
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| 0.4731 | 17.72 | 3700 | 0.6363 | 0.8742 | 0.8812 | 0.8742 | 0.8724 | 0.9128 |
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| 137 |
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| 0.4731 | 17.96 | 3750 | 0.6116 | 0.8854 | 0.8926 | 0.8854 | 0.8841 | 0.9199 |
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| 138 |
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| 0.4605 | 18.2 | 3800 | 0.6574 | 0.8794 | 0.8897 | 0.8794 | 0.8778 | 0.9161 |
|
| 139 |
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| 0.4605 | 18.44 | 3850 | 0.6271 | 0.8749 | 0.8842 | 0.8749 | 0.8731 | 0.9135 |
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| 140 |
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| 0.4605 | 18.68 | 3900 | 0.6418 | 0.8749 | 0.8830 | 0.8749 | 0.8736 | 0.9139 |
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| 141 |
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| 0.4605 | 18.92 | 3950 | 0.6398 | 0.8704 | 0.8825 | 0.8704 | 0.8688 | 0.9103 |
|
| 142 |
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| 0.4339 | 19.16 | 4000 | 0.6366 | 0.8689 | 0.8760 | 0.8689 | 0.8664 | 0.9085 |
|
| 143 |
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| 0.4339 | 19.4 | 4050 | 0.6164 | 0.8727 | 0.8824 | 0.8727 | 0.8716 | 0.9110 |
|
| 144 |
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| 0.4339 | 19.64 | 4100 | 0.6044 | 0.8846 | 0.8904 | 0.8846 | 0.8837 | 0.9190 |
|
| 145 |
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| 0.4339 | 19.88 | 4150 | 0.6749 | 0.8742 | 0.8807 | 0.8742 | 0.8716 | 0.9123 |
|
| 146 |
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| 0.4057 | 20.12 | 4200 | 0.7049 | 0.8637 | 0.8748 | 0.8637 | 0.8617 | 0.9059 |
|
| 147 |
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| 0.4057 | 20.36 | 4250 | 0.6698 | 0.8727 | 0.8821 | 0.8727 | 0.8718 | 0.9116 |
|
| 148 |
+
| 0.4057 | 20.6 | 4300 | 0.6165 | 0.8779 | 0.8900 | 0.8779 | 0.8776 | 0.9146 |
|
| 149 |
+
| 0.4057 | 20.84 | 4350 | 0.5957 | 0.8697 | 0.8791 | 0.8697 | 0.8688 | 0.9087 |
|
| 150 |
+
| 0.4144 | 21.08 | 4400 | 0.6662 | 0.8644 | 0.8741 | 0.8644 | 0.8644 | 0.9047 |
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| 151 |
+
| 0.4144 | 21.32 | 4450 | 0.7379 | 0.8487 | 0.8573 | 0.8487 | 0.8481 | 0.8942 |
|
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|
| 152 |
|
| 153 |
|
| 154 |
### Framework versions
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 378386248
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3a42506fed67750f437bc1892bc7470f9d0326c4907ba018713229490336b9b8
|
| 3 |
size 378386248
|
runs/Jul27_04-09-15_LAPTOP-1GID9RGH/events.out.tfevents.1722028156.LAPTOP-1GID9RGH.2524.2
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5007df202d957b12c9eb74cd6a7bb35aec55bab9acd4dbcf60c0f287dcb9729c
|
| 3 |
+
size 60516
|
runs/Jul27_04-09-15_LAPTOP-1GID9RGH/events.out.tfevents.1722030477.LAPTOP-1GID9RGH.2524.3
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a71e923bd045ac9b74d84545008a8b1f24f83c06f5144813c814da39bc3f933a
|
| 3 |
+
size 610
|