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---
language:
- all
license: apache-2.0
tags:
- minds14
- google/xtreme_s
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: xtreme_s_xlsr_t5lephone-small_minds14.en-all
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. -->
# xtreme_s_xlsr_t5lephone-small_minds14.en-all
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - MINDS14.ALL dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5979
- F1: 0.8918
- Accuracy: 0.8921
## 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: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 150.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:------:|:--------:|
| 2.3561 | 2.98 | 200 | 2.5464 | 0.0681 | 0.1334 |
| 1.1851 | 5.97 | 400 | 1.5056 | 0.5583 | 0.5861 |
| 1.2805 | 8.95 | 600 | 1.1397 | 0.7106 | 0.7044 |
| 1.0801 | 11.94 | 800 | 0.9863 | 0.7132 | 0.7198 |
| 0.9285 | 14.92 | 1000 | 0.9912 | 0.7037 | 0.7139 |
| 0.4164 | 17.91 | 1200 | 0.8226 | 0.7743 | 0.7741 |
| 0.7669 | 20.89 | 1400 | 0.8131 | 0.7783 | 0.7788 |
| 0.4606 | 23.88 | 1600 | 0.8314 | 0.7879 | 0.7792 |
| 0.6975 | 26.86 | 1800 | 0.7667 | 0.7927 | 0.7939 |
| 0.9913 | 29.85 | 2000 | 0.9207 | 0.7734 | 0.7707 |
| 0.2307 | 32.83 | 2200 | 0.7651 | 0.8072 | 0.8086 |
| 0.1412 | 35.82 | 2400 | 0.7132 | 0.8352 | 0.8311 |
| 0.2141 | 38.8 | 2600 | 0.7551 | 0.8276 | 0.8262 |
| 0.2169 | 41.79 | 2800 | 0.7900 | 0.8148 | 0.8160 |
| 0.3942 | 44.77 | 3000 | 0.8621 | 0.8130 | 0.8042 |
| 0.2306 | 47.76 | 3200 | 0.6788 | 0.8264 | 0.8253 |
| 0.0975 | 50.74 | 3400 | 0.7236 | 0.8295 | 0.8289 |
| 0.0062 | 53.73 | 3600 | 0.6872 | 0.8286 | 0.8277 |
| 0.1781 | 56.71 | 3800 | 0.6990 | 0.8393 | 0.8390 |
| 0.0309 | 59.7 | 4000 | 0.6348 | 0.8496 | 0.8500 |
| 0.0026 | 62.68 | 4200 | 0.6737 | 0.8585 | 0.8566 |
| 0.0043 | 65.67 | 4400 | 0.7780 | 0.8416 | 0.8387 |
| 0.0032 | 68.65 | 4600 | 0.6899 | 0.8482 | 0.8461 |
| 0.0302 | 71.64 | 4800 | 0.6813 | 0.8515 | 0.8495 |
| 0.0027 | 74.62 | 5000 | 0.7163 | 0.8530 | 0.8529 |
| 0.1165 | 77.61 | 5200 | 0.6249 | 0.8603 | 0.8595 |
| 0.0021 | 80.59 | 5400 | 0.6747 | 0.8588 | 0.8578 |
| 0.2558 | 83.58 | 5600 | 0.7514 | 0.8581 | 0.8581 |
| 0.0162 | 86.57 | 5800 | 0.6782 | 0.8667 | 0.8664 |
| 0.1929 | 89.55 | 6000 | 0.6371 | 0.8615 | 0.8600 |
| 0.0621 | 92.54 | 6200 | 0.8079 | 0.8600 | 0.8607 |
| 0.0017 | 95.52 | 6400 | 0.7072 | 0.8678 | 0.8669 |
| 0.0008 | 98.51 | 6600 | 0.7323 | 0.8572 | 0.8541 |
| 0.1655 | 101.49 | 6800 | 0.6953 | 0.8521 | 0.8505 |
| 0.01 | 104.48 | 7000 | 0.7149 | 0.8665 | 0.8674 |
| 0.0135 | 107.46 | 7200 | 0.8990 | 0.8523 | 0.8488 |
| 0.0056 | 110.45 | 7400 | 0.7320 | 0.8673 | 0.8664 |
| 0.0023 | 113.43 | 7600 | 0.7108 | 0.8700 | 0.8705 |
| 0.0025 | 116.42 | 7800 | 0.6464 | 0.8818 | 0.8820 |
| 0.0003 | 119.4 | 8000 | 0.6985 | 0.8706 | 0.8713 |
| 0.0048 | 122.39 | 8200 | 0.6620 | 0.8765 | 0.8740 |
| 0.2335 | 125.37 | 8400 | 0.6515 | 0.8832 | 0.8828 |
| 0.0005 | 128.36 | 8600 | 0.6961 | 0.8776 | 0.8762 |
| 0.0003 | 131.34 | 8800 | 0.5990 | 0.8878 | 0.8882 |
| 0.0002 | 134.33 | 9000 | 0.6236 | 0.8887 | 0.8889 |
| 0.002 | 137.31 | 9200 | 0.6671 | 0.8847 | 0.8845 |
| 0.0002 | 140.3 | 9400 | 0.5970 | 0.8931 | 0.8935 |
| 0.0002 | 143.28 | 9600 | 0.6095 | 0.8906 | 0.8913 |
| 0.0002 | 146.27 | 9800 | 0.6056 | 0.8910 | 0.8913 |
| 0.0002 | 149.25 | 10000 | 0.5979 | 0.8918 | 0.8921 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
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