tgrhn
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wav2vec2-turkish-5

@@ -1,199 +1,170 @@
 ---
-library_name: transformers
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 ---
+language:
+- tr
+license: apache-2.0
+base_model: facebook/wav2vec2-xls-r-300m
+tags:
+- generated_from_trainer
+datasets:
+- mozilla-foundation/common_voice_17
+model-index:
+- name: 'Wav2Vec2-XLS-TR '
+  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-XLS-TR
+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 17 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3285
+## 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: 32
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 30
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch   | Step  | Validation Loss |
+|:-------------:|:-------:|:-----:|:---------------:|
+| 5.5445        | 0.2757  | 400   | 1.2160          |
+| 0.7341        | 0.5513  | 800   | 0.6683          |
+| 0.5195        | 0.8270  | 1200  | 0.5313          |
+| 0.4561        | 1.1027  | 1600  | 0.4837          |
+| 0.4016        | 1.3784  | 2000  | 0.4725          |
+| 0.3945        | 1.6540  | 2400  | 0.4570          |
+| 0.3756        | 1.9297  | 2800  | 0.4284          |
+| 0.3341        | 2.2054  | 3200  | 0.4283          |
+| 0.3316        | 2.4810  | 3600  | 0.3945          |
+| 0.3333        | 2.7567  | 4000  | 0.4283          |
+| 0.3224        | 3.0324  | 4400  | 0.3993          |
+| 0.2924        | 3.3081  | 4800  | 0.4216          |
+| 0.3012        | 3.5837  | 5200  | 0.3729          |
+| 0.2889        | 3.8594  | 5600  | 0.3962          |
+| 0.2767        | 4.1351  | 6000  | 0.4037          |
+| 0.2714        | 4.4108  | 6400  | 0.3740          |
+| 0.2721        | 4.6864  | 6800  | 0.3821          |
+| 0.2673        | 4.9621  | 7200  | 0.3580          |
+| 0.2407        | 5.2378  | 7600  | 0.3758          |
+| 0.2525        | 5.5134  | 8000  | 0.4067          |
+| 0.2477        | 5.7891  | 8400  | 0.3675          |
+| 0.2433        | 6.0648  | 8800  | 0.3653          |
+| 0.229         | 6.3405  | 9200  | 0.3485          |
+| 0.2326        | 6.6161  | 9600  | 0.3674          |
+| 0.2288        | 6.8918  | 10000 | 0.3664          |
+| 0.2199        | 7.1675  | 10400 | 0.3656          |
+| 0.2094        | 7.4431  | 10800 | 0.3389          |
+| 0.2191        | 7.7188  | 11200 | 0.3446          |
+| 0.2149        | 7.9945  | 11600 | 0.3489          |
+| 0.1967        | 8.2702  | 12000 | 0.3482          |
+| 0.2042        | 8.5458  | 12400 | 0.3464          |
+| 0.2043        | 8.8215  | 12800 | 0.3517          |
+| 0.1919        | 9.0972  | 13200 | 0.3408          |
+| 0.1844        | 9.3728  | 13600 | 0.3465          |
+| 0.1906        | 9.6485  | 14000 | 0.3349          |
+| 0.1868        | 9.9242  | 14400 | 0.3282          |
+| 0.1732        | 10.1999 | 14800 | 0.3604          |
+| 0.1715        | 10.4755 | 15200 | 0.3413          |
+| 0.1734        | 10.7512 | 15600 | 0.3309          |
+| 0.1785        | 11.0269 | 16000 | 0.3351          |
+| 0.1643        | 11.3025 | 16400 | 0.3326          |
+| 0.1603        | 11.5782 | 16800 | 0.3205          |
+| 0.1662        | 11.8539 | 17200 | 0.3332          |
+| 0.1561        | 12.1296 | 17600 | 0.3311          |
+| 0.1512        | 12.4052 | 18000 | 0.3322          |
+| 0.1509        | 12.6809 | 18400 | 0.3227          |
+| 0.1516        | 12.9566 | 18800 | 0.3338          |
+| 0.1493        | 13.2323 | 19200 | 0.3439          |
+| 0.1426        | 13.5079 | 19600 | 0.3447          |
+| 0.143         | 13.7836 | 20000 | 0.3299          |
+| 0.1398        | 14.0593 | 20400 | 0.3273          |
+| 0.1351        | 14.3349 | 20800 | 0.3281          |
+| 0.1384        | 14.6106 | 21200 | 0.3333          |
+| 0.1335        | 14.8863 | 21600 | 0.3311          |
+| 0.1291        | 15.1620 | 22000 | 0.3230          |
+| 0.1259        | 15.4376 | 22400 | 0.3301          |
+| 0.1294        | 15.7133 | 22800 | 0.3446          |
+| 0.1269        | 15.9890 | 23200 | 0.3271          |
+| 0.1196        | 16.2646 | 23600 | 0.3204          |
+| 0.1166        | 16.5403 | 24000 | 0.3031          |
+| 0.12          | 16.8160 | 24400 | 0.3258          |
+| 0.1163        | 17.0917 | 24800 | 0.3408          |
+| 0.1101        | 17.3673 | 25200 | 0.3246          |
+| 0.1142        | 17.6430 | 25600 | 0.3201          |
+| 0.1121        | 17.9187 | 26000 | 0.3198          |
+| 0.1044        | 18.1943 | 26400 | 0.3441          |
+| 0.105         | 18.4700 | 26800 | 0.3441          |
+| 0.1032        | 18.7457 | 27200 | 0.3252          |
+| 0.104         | 19.0214 | 27600 | 0.3170          |
+| 0.0968        | 19.2970 | 28000 | 0.3363          |
+| 0.0946        | 19.5727 | 28400 | 0.3100          |
+| 0.0974        | 19.8484 | 28800 | 0.3128          |
+| 0.0889        | 20.1241 | 29200 | 0.3325          |
+| 0.0887        | 20.3997 | 29600 | 0.3276          |
+| 0.0891        | 20.6754 | 30000 | 0.3253          |
+| 0.0937        | 20.9511 | 30400 | 0.3270          |
+| 0.0854        | 21.2267 | 30800 | 0.3294          |
+| 0.0864        | 21.5024 | 31200 | 0.3352          |
+| 0.0864        | 21.7781 | 31600 | 0.3279          |
+| 0.0829        | 22.0538 | 32000 | 0.3245          |
+| 0.0799        | 22.3294 | 32400 | 0.3329          |
+| 0.0811        | 22.6051 | 32800 | 0.3295          |
+| 0.0777        | 22.8808 | 33200 | 0.3204          |
+| 0.074         | 23.1564 | 33600 | 0.3286          |
+| 0.0762        | 23.4321 | 34000 | 0.3326          |
+| 0.0765        | 23.7078 | 34400 | 0.3061          |
+| 0.0745        | 23.9835 | 34800 | 0.3313          |
+| 0.0702        | 24.2591 | 35200 | 0.3131          |
+| 0.0684        | 24.5348 | 35600 | 0.3236          |
+| 0.0689        | 24.8105 | 36000 | 0.3152          |
+| 0.0716        | 25.0861 | 36400 | 0.3272          |
+| 0.0609        | 25.3618 | 36800 | 0.3294          |
+| 0.0607        | 25.6375 | 37200 | 0.3366          |
+| 0.0624        | 25.9132 | 37600 | 0.3215          |
+| 0.0614        | 26.1888 | 38000 | 0.3236          |
+| 0.0599        | 26.4645 | 38400 | 0.3321          |
+| 0.0608        | 26.7402 | 38800 | 0.3311          |
+| 0.0558        | 27.0159 | 39200 | 0.3368          |
+| 0.0589        | 27.2915 | 39600 | 0.3217          |
+| 0.0573        | 27.5672 | 40000 | 0.3320          |
+| 0.0546        | 27.8429 | 40400 | 0.3316          |
+| 0.0526        | 28.1185 | 40800 | 0.3285          |
+| 0.0501        | 28.3942 | 41200 | 0.3287          |
+| 0.0515        | 28.6699 | 41600 | 0.3261          |
+| 0.0507        | 28.9456 | 42000 | 0.3316          |
+| 0.0539        | 29.2212 | 42400 | 0.3285          |
+| 0.0489        | 29.4969 | 42800 | 0.3319          |
+| 0.053         | 29.7726 | 43200 | 0.3285          |
+### Framework versions
+- Transformers 4.41.2
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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