update model card README.md

README.md

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+---
+language:
+- tr
+license: apache-2.0
+tags:
+- automatic-speech-recognition
+- common_voice
+- generated_from_trainer
+datasets:
+- common_voice
+model-index:
+- name: wav2vec2-large-xls-r-300m-common_voice-tr-ft
+  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. -->
+
+# wav2vec2-large-xls-r-300m-common_voice-tr-ft
+
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - TR dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3644
+- Wer: 0.3394
+- Cer: 0.0811
+
+## 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: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- total_train_batch_size: 32
+- total_eval_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 100.0
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
+| 1.6613        | 4.59  | 500   | 0.8079          | 0.8542 | 0.2504 |
+| 1.3496        | 9.17  | 1000  | 0.4729          | 0.5968 | 0.1518 |
+| 1.1003        | 13.76 | 1500  | 0.4106          | 0.5225 | 0.1357 |
+| 1.1532        | 18.35 | 2000  | 0.3957          | 0.4978 | 0.1256 |
+| 1.0305        | 22.94 | 2500  | 0.3764          | 0.5008 | 0.1291 |
+| 0.8303        | 27.52 | 3000  | 0.3826          | 0.5113 | 0.1292 |
+| 0.9115        | 32.11 | 3500  | 0.3819          | 0.4324 | 0.1070 |
+| 0.8193        | 36.7  | 4000  | 0.3694          | 0.4223 | 0.1036 |
+| 0.8948        | 41.28 | 4500  | 0.3714          | 0.4100 | 0.1005 |
+| 0.774         | 45.87 | 5000  | 0.3558          | 0.3923 | 0.0971 |
+| 0.8194        | 50.46 | 5500  | 0.3729          | 0.4603 | 0.1180 |
+| 0.8616        | 55.05 | 6000  | 0.3616          | 0.3908 | 0.0963 |
+| 0.7901        | 59.63 | 6500  | 0.3575          | 0.3837 | 0.0952 |
+| 0.778         | 64.22 | 7000  | 0.3732          | 0.3790 | 0.0928 |
+| 0.7238        | 68.81 | 7500  | 0.3674          | 0.3734 | 0.0904 |
+| 0.6985        | 73.39 | 8000  | 0.3627          | 0.3615 | 0.0863 |
+| 0.5889        | 77.98 | 8500  | 0.3705          | 0.3548 | 0.0858 |
+| 0.5447        | 82.57 | 9000  | 0.3678          | 0.3534 | 0.0854 |
+| 0.4763        | 87.16 | 9500  | 0.3627          | 0.3509 | 0.0840 |
+| 0.3544        | 91.74 | 10000 | 0.3690          | 0.3495 | 0.0834 |
+| 0.4879        | 96.33 | 10500 | 0.3683          | 0.3418 | 0.0820 |
+
+
+### Framework versions
+
+- Transformers 4.13.0.dev0
+- Pytorch 1.9.0+cu111
+- Datasets 1.15.2.dev0
+- Tokenizers 0.10.3