<!-- 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. -->
xtreme_s_xlsr_300m_fleurs_langid_quicker_warmup
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the xtreme_s dataset. It achieves the following results on the evaluation set:
- Loss: 1.9765
- Accuracy: 0.6199
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: 0.0003
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| 0.6644 | 0.26 | 1000 | 0.3071 | 3.2482 |
| 0.394 | 0.52 | 2000 | 0.5948 | 1.8833 |
| 0.1034 | 0.78 | 3000 | 0.6297 | 1.5852 |
| 0.1088 | 1.04 | 4000 | 0.5992 | 1.7903 |
| 0.0032 | 1.3 | 5000 | 0.6356 | 1.6219 |
| 0.1813 | 1.56 | 6000 | 0.5788 | 1.8168 |
| 0.0654 | 1.82 | 7000 | 0.6234 | 1.6089 |
| 0.0144 | 2.08 | 8000 | 0.6424 | 1.6071 |
| 0.0019 | 2.34 | 9000 | 0.5822 | 1.7820 |
| 0.0159 | 2.6 | 10000 | 0.6043 | 1.8407 |
| 0.0029 | 2.86 | 11000 | 0.5845 | 1.8600 |
| 0.0458 | 3.12 | 12000 | 0.6299 | 1.6591 |
| 0.013 | 3.38 | 13000 | 0.5903 | 2.0788 |
| 0.003 | 3.64 | 14000 | 0.6188 | 1.7645 |
| 0.0015 | 3.9 | 15000 | 0.6328 | 1.7739 |
| 0.0003 | 4.16 | 16000 | 0.6072 | 1.8742 |
| 0.0005 | 4.42 | 17000 | 0.6231 | 1.7102 |
| 0.006 | 4.68 | 18000 | 0.6122 | 1.6909 |
| 0.2367 | 4.93 | 19000 | 0.6029 | 1.9891 |
| 0.005 | 5.19 | 20000 | 0.6220 | 1.7245 |
| 0.0813 | 5.45 | 21000 | 0.5739 | 2.0495 |
| 0.1233 | 5.71 | 22000 | 0.6104 | 1.9601 |
| 0.0003 | 5.97 | 23000 | 0.5924 | 1.8881 |
| 0.0003 | 6.23 | 24000 | 0.6055 | 1.9568 |
| 0.0001 | 6.49 | 25000 | 0.6086 | 1.8489 |
| 0.2198 | 6.75 | 26000 | 0.6292 | 1.8048 |
| 0.0261 | 7.01 | 27000 | 2.0284 | 0.5989 |
| 0.0001 | 7.27 | 28000 | 1.7323 | 0.6431 |
| 0.0001 | 7.53 | 29000 | 1.9329 | 0.6310 |
| 0.0011 | 7.79 | 30000 | 1.9256 | 0.6107 |
| 0.0933 | 8.05 | 31000 | 2.3915 | 0.5896 |
| 0.0001 | 8.31 | 32000 | 1.9948 | 0.6021 |
| 0.0003 | 8.57 | 33000 | 1.9518 | 0.6126 |
| 0.0005 | 8.83 | 34000 | 1.8935 | 0.6243 |
| 0.0 | 9.09 | 35000 | 2.0177 | 0.6144 |
| 0.0002 | 9.35 | 36000 | 2.0234 | 0.6174 |
| 0.0 | 9.61 | 37000 | 1.9568 | 0.6216 |
| 0.0 | 9.87 | 38000 | 1.9765 | 0.6199 |
Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 1.18.4.dev0
- Tokenizers 0.11.6
