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---
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hubert-classifier-aug-80
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hubert-classifier-aug-80
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7879
- Accuracy: 0.8248
- Precision: 0.8454
- Recall: 0.8248
- F1: 0.8211
- Binary: 0.8778
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| No log | 0.22 | 50 | 4.4263 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1245 |
| No log | 0.43 | 100 | 4.4241 | 0.0148 | 0.0002 | 0.0148 | 0.0004 | 0.1261 |
| No log | 0.65 | 150 | 4.4226 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1333 |
| No log | 0.86 | 200 | 4.0724 | 0.0310 | 0.0015 | 0.0310 | 0.0027 | 0.2334 |
| No log | 1.08 | 250 | 3.9071 | 0.0270 | 0.0009 | 0.0270 | 0.0016 | 0.2287 |
| No log | 1.29 | 300 | 3.8704 | 0.0283 | 0.0009 | 0.0283 | 0.0017 | 0.2327 |
| No log | 1.51 | 350 | 3.8439 | 0.0256 | 0.0007 | 0.0256 | 0.0014 | 0.2306 |
| No log | 1.73 | 400 | 3.8189 | 0.0256 | 0.0015 | 0.0256 | 0.0025 | 0.2330 |
| No log | 1.94 | 450 | 3.7439 | 0.0391 | 0.0058 | 0.0391 | 0.0068 | 0.2950 |
| 4.1116 | 2.16 | 500 | 3.5560 | 0.0404 | 0.0033 | 0.0404 | 0.0046 | 0.3185 |
| 4.1116 | 2.37 | 550 | 3.4895 | 0.0404 | 0.0035 | 0.0404 | 0.0059 | 0.3150 |
| 4.1116 | 2.59 | 600 | 3.3966 | 0.0512 | 0.0053 | 0.0512 | 0.0094 | 0.3253 |
| 4.1116 | 2.8 | 650 | 3.3257 | 0.0687 | 0.0157 | 0.0687 | 0.0175 | 0.3420 |
| 4.1116 | 3.02 | 700 | 3.2077 | 0.0593 | 0.0106 | 0.0593 | 0.0156 | 0.3136 |
| 4.1116 | 3.24 | 750 | 3.1384 | 0.0957 | 0.0225 | 0.0957 | 0.0302 | 0.3609 |
| 4.1116 | 3.45 | 800 | 3.1351 | 0.0943 | 0.0273 | 0.0943 | 0.0364 | 0.3597 |
| 4.1116 | 3.67 | 850 | 2.9107 | 0.1253 | 0.0606 | 0.1253 | 0.0646 | 0.3829 |
| 4.1116 | 3.88 | 900 | 2.8937 | 0.1509 | 0.0677 | 0.1509 | 0.0712 | 0.3927 |
| 4.1116 | 4.1 | 950 | 2.7737 | 0.1806 | 0.1052 | 0.1806 | 0.1128 | 0.4205 |
| 3.2639 | 4.31 | 1000 | 2.6901 | 0.1658 | 0.0774 | 0.1658 | 0.0851 | 0.4117 |
| 3.2639 | 4.53 | 1050 | 2.5441 | 0.2183 | 0.1329 | 0.2183 | 0.1424 | 0.4482 |
| 3.2639 | 4.75 | 1100 | 2.4408 | 0.2197 | 0.1407 | 0.2197 | 0.1382 | 0.4488 |
| 3.2639 | 4.96 | 1150 | 2.4113 | 0.2278 | 0.1691 | 0.2278 | 0.1517 | 0.4562 |
| 3.2639 | 5.18 | 1200 | 2.2525 | 0.2790 | 0.2052 | 0.2790 | 0.1916 | 0.4904 |
| 3.2639 | 5.39 | 1250 | 2.2126 | 0.2817 | 0.2064 | 0.2817 | 0.1939 | 0.4962 |
| 3.2639 | 5.61 | 1300 | 2.1644 | 0.2951 | 0.2583 | 0.2951 | 0.2264 | 0.5039 |
| 3.2639 | 5.83 | 1350 | 2.1951 | 0.3275 | 0.2823 | 0.3275 | 0.2698 | 0.5199 |
| 3.2639 | 6.04 | 1400 | 1.9989 | 0.3666 | 0.3230 | 0.3666 | 0.3087 | 0.5532 |
| 3.2639 | 6.26 | 1450 | 1.8910 | 0.3962 | 0.3735 | 0.3962 | 0.3340 | 0.5749 |
| 2.4809 | 6.47 | 1500 | 1.8342 | 0.4084 | 0.4092 | 0.4084 | 0.3542 | 0.5838 |
| 2.4809 | 6.69 | 1550 | 1.8166 | 0.4272 | 0.4345 | 0.4272 | 0.3809 | 0.5954 |
| 2.4809 | 6.9 | 1600 | 1.6498 | 0.4838 | 0.4594 | 0.4838 | 0.4297 | 0.6345 |
| 2.4809 | 7.12 | 1650 | 1.6093 | 0.5040 | 0.5262 | 0.5040 | 0.4666 | 0.6515 |
| 2.4809 | 7.34 | 1700 | 1.5510 | 0.5296 | 0.5257 | 0.5296 | 0.4896 | 0.6689 |
| 2.4809 | 7.55 | 1750 | 1.5003 | 0.5175 | 0.5164 | 0.5175 | 0.4669 | 0.6621 |
| 2.4809 | 7.77 | 1800 | 1.4597 | 0.5270 | 0.5263 | 0.5270 | 0.4861 | 0.6671 |
| 2.4809 | 7.98 | 1850 | 1.3801 | 0.5916 | 0.6024 | 0.5916 | 0.5598 | 0.7115 |
| 2.4809 | 8.2 | 1900 | 1.3262 | 0.5863 | 0.5970 | 0.5863 | 0.5574 | 0.7101 |
| 2.4809 | 8.41 | 1950 | 1.2342 | 0.5943 | 0.5938 | 0.5943 | 0.5648 | 0.7163 |
| 1.8737 | 8.63 | 2000 | 1.2114 | 0.6173 | 0.6210 | 0.6173 | 0.5957 | 0.7333 |
| 1.8737 | 8.85 | 2050 | 1.1831 | 0.6321 | 0.6535 | 0.6321 | 0.6072 | 0.7414 |
| 1.8737 | 9.06 | 2100 | 1.1501 | 0.6563 | 0.6882 | 0.6563 | 0.6398 | 0.7567 |
| 1.8737 | 9.28 | 2150 | 1.0732 | 0.6941 | 0.7109 | 0.6941 | 0.6802 | 0.7841 |
| 1.8737 | 9.49 | 2200 | 1.1194 | 0.6604 | 0.6696 | 0.6604 | 0.6424 | 0.7615 |
| 1.8737 | 9.71 | 2250 | 0.9827 | 0.7035 | 0.7331 | 0.7035 | 0.6924 | 0.7926 |
| 1.8737 | 9.92 | 2300 | 0.9956 | 0.7156 | 0.7381 | 0.7156 | 0.7047 | 0.8007 |
| 1.8737 | 10.14 | 2350 | 1.0312 | 0.6698 | 0.7095 | 0.6698 | 0.6552 | 0.7685 |
| 1.8737 | 10.36 | 2400 | 0.9753 | 0.7197 | 0.7465 | 0.7197 | 0.7070 | 0.8043 |
| 1.8737 | 10.57 | 2450 | 0.9825 | 0.7237 | 0.7322 | 0.7237 | 0.7118 | 0.8074 |
| 1.4378 | 10.79 | 2500 | 0.9829 | 0.6927 | 0.7234 | 0.6927 | 0.6821 | 0.7853 |
| 1.4378 | 11.0 | 2550 | 0.8897 | 0.7251 | 0.7515 | 0.7251 | 0.7152 | 0.8066 |
| 1.4378 | 11.22 | 2600 | 0.8627 | 0.7345 | 0.7624 | 0.7345 | 0.7277 | 0.8123 |
| 1.4378 | 11.43 | 2650 | 0.8772 | 0.7264 | 0.7602 | 0.7264 | 0.7228 | 0.8074 |
| 1.4378 | 11.65 | 2700 | 0.9209 | 0.7399 | 0.7622 | 0.7399 | 0.7321 | 0.8164 |
| 1.4378 | 11.87 | 2750 | 0.8737 | 0.7412 | 0.7623 | 0.7412 | 0.7345 | 0.8181 |
| 1.4378 | 12.08 | 2800 | 0.8638 | 0.7439 | 0.7632 | 0.7439 | 0.7370 | 0.8189 |
| 1.4378 | 12.3 | 2850 | 0.8525 | 0.7547 | 0.7763 | 0.7547 | 0.7492 | 0.8290 |
| 1.4378 | 12.51 | 2900 | 0.8238 | 0.7466 | 0.7598 | 0.7466 | 0.7382 | 0.8209 |
| 1.4378 | 12.73 | 2950 | 0.8192 | 0.7507 | 0.7771 | 0.7507 | 0.7446 | 0.8241 |
| 1.1771 | 12.94 | 3000 | 0.7660 | 0.7642 | 0.7801 | 0.7642 | 0.7589 | 0.8338 |
| 1.1771 | 13.16 | 3050 | 0.8528 | 0.7453 | 0.7676 | 0.7453 | 0.7369 | 0.8213 |
| 1.1771 | 13.38 | 3100 | 0.7580 | 0.7776 | 0.7881 | 0.7776 | 0.7707 | 0.8425 |
| 1.1771 | 13.59 | 3150 | 0.8186 | 0.7615 | 0.7849 | 0.7615 | 0.7536 | 0.8345 |
| 1.1771 | 13.81 | 3200 | 0.7512 | 0.7871 | 0.8057 | 0.7871 | 0.7808 | 0.8519 |
| 1.1771 | 14.02 | 3250 | 0.7426 | 0.7763 | 0.7965 | 0.7763 | 0.7710 | 0.8439 |
| 1.1771 | 14.24 | 3300 | 0.8203 | 0.7695 | 0.7827 | 0.7695 | 0.7619 | 0.8407 |
| 1.1771 | 14.46 | 3350 | 0.7871 | 0.7682 | 0.7878 | 0.7682 | 0.7590 | 0.8377 |
| 1.1771 | 14.67 | 3400 | 0.7761 | 0.7830 | 0.8044 | 0.7830 | 0.7733 | 0.8470 |
| 1.1771 | 14.89 | 3450 | 0.8547 | 0.7763 | 0.7965 | 0.7763 | 0.7731 | 0.8451 |
| 0.9984 | 15.1 | 3500 | 0.7879 | 0.7709 | 0.7922 | 0.7709 | 0.7633 | 0.8400 |
| 0.9984 | 15.32 | 3550 | 0.7582 | 0.8086 | 0.8235 | 0.8086 | 0.8037 | 0.8655 |
| 0.9984 | 15.53 | 3600 | 0.7084 | 0.7938 | 0.8074 | 0.7938 | 0.7872 | 0.8555 |
| 0.9984 | 15.75 | 3650 | 0.7424 | 0.7911 | 0.8099 | 0.7911 | 0.7864 | 0.8553 |
| 0.9984 | 15.97 | 3700 | 0.7255 | 0.8127 | 0.8274 | 0.8127 | 0.8090 | 0.8706 |
| 0.9984 | 16.18 | 3750 | 0.6903 | 0.8059 | 0.8216 | 0.8059 | 0.8011 | 0.8646 |
| 0.9984 | 16.4 | 3800 | 0.7078 | 0.8100 | 0.8324 | 0.8100 | 0.8043 | 0.8689 |
| 0.9984 | 16.61 | 3850 | 0.7843 | 0.7992 | 0.8218 | 0.7992 | 0.7940 | 0.8604 |
| 0.9984 | 16.83 | 3900 | 0.7239 | 0.7965 | 0.8226 | 0.7965 | 0.7936 | 0.8581 |
| 0.9984 | 17.04 | 3950 | 0.7097 | 0.8127 | 0.8283 | 0.8127 | 0.8092 | 0.8679 |
| 0.8969 | 17.26 | 4000 | 0.8020 | 0.7951 | 0.8135 | 0.7951 | 0.7914 | 0.8566 |
| 0.8969 | 17.48 | 4050 | 0.6915 | 0.8275 | 0.8477 | 0.8275 | 0.8242 | 0.8792 |
| 0.8969 | 17.69 | 4100 | 0.7548 | 0.8113 | 0.8321 | 0.8113 | 0.8071 | 0.8685 |
| 0.8969 | 17.91 | 4150 | 0.7284 | 0.8073 | 0.8293 | 0.8073 | 0.8036 | 0.8673 |
| 0.8969 | 18.12 | 4200 | 0.7304 | 0.8127 | 0.8276 | 0.8127 | 0.8092 | 0.8687 |
| 0.8969 | 18.34 | 4250 | 0.7169 | 0.8154 | 0.8319 | 0.8154 | 0.8109 | 0.8706 |
| 0.8969 | 18.55 | 4300 | 0.7189 | 0.8194 | 0.8375 | 0.8194 | 0.8173 | 0.8736 |
| 0.8969 | 18.77 | 4350 | 0.8506 | 0.7790 | 0.8073 | 0.7790 | 0.7718 | 0.8457 |
| 0.8969 | 18.99 | 4400 | 0.7322 | 0.8248 | 0.8436 | 0.8248 | 0.8211 | 0.8788 |
| 0.8969 | 19.2 | 4450 | 0.7497 | 0.8032 | 0.8212 | 0.8032 | 0.7997 | 0.8627 |
| 0.8076 | 19.42 | 4500 | 0.7879 | 0.8248 | 0.8454 | 0.8248 | 0.8211 | 0.8778 |
| 0.8076 | 19.63 | 4550 | 0.8195 | 0.8073 | 0.8314 | 0.8073 | 0.8032 | 0.8660 |
| 0.8076 | 19.85 | 4600 | 0.8176 | 0.8059 | 0.8249 | 0.8059 | 0.8032 | 0.8651 |
| 0.8076 | 20.06 | 4650 | 0.7699 | 0.8221 | 0.8375 | 0.8221 | 0.8180 | 0.8753 |
| 0.8076 | 20.28 | 4700 | 0.7316 | 0.8181 | 0.8407 | 0.8181 | 0.8162 | 0.8722 |
| 0.8076 | 20.5 | 4750 | 0.7205 | 0.8086 | 0.8272 | 0.8086 | 0.8060 | 0.8670 |
| 0.8076 | 20.71 | 4800 | 0.7689 | 0.7965 | 0.8150 | 0.7965 | 0.7930 | 0.8574 |
| 0.8076 | 20.93 | 4850 | 0.7828 | 0.8127 | 0.8297 | 0.8127 | 0.8101 | 0.8689 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1
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