---
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