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xlsr-czech

@@ -1,199 +1,142 @@
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 ---
+license: apache-2.0
+base_model: facebook/wav2vec2-xls-r-300m
+tags:
+- generated_from_trainer
+datasets:
+- common_voice_17_0
+metrics:
+- wer
+model-index:
+- name: xlsr-czech
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_17_0
+      type: common_voice_17_0
+      config: cs
+      split: validation
+      args: cs
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.17594345952554907
 ---
+<!-- 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. -->
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/badr-nlp/xlsr-continual-finetuning-polish/runs/c0c7ty73)
+# xlsr-czech
+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_0 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2040
+- Wer: 0.1759
+- Cer: 0.0387
+## 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: 16
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |
+|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
+| 5.9885        | 0.1589 | 100  | 4.5039          | 1.0    | 1.0    |
+| 3.3721        | 0.3177 | 200  | 3.3649          | 1.0    | 1.0    |
+| 3.4173        | 0.4766 | 300  | 3.2849          | 1.0000 | 0.9888 |
+| 1.5357        | 0.6354 | 400  | 1.2694          | 0.9288 | 0.3102 |
+| 0.9318        | 0.7943 | 500  | 0.6914          | 0.6972 | 0.1799 |
+| 0.8233        | 0.9531 | 600  | 0.5004          | 0.5664 | 0.1342 |
+| 0.3873        | 1.1120 | 700  | 0.3576          | 0.3941 | 0.0903 |
+| 0.2506        | 1.2708 | 800  | 0.3360          | 0.3798 | 0.0861 |
+| 0.2435        | 1.4297 | 900  | 0.2956          | 0.3328 | 0.0744 |
+| 0.2828        | 1.5886 | 1000 | 0.2773          | 0.3156 | 0.0710 |
+| 0.2454        | 1.7474 | 1100 | 0.2764          | 0.3138 | 0.0699 |
+| 0.2607        | 1.9063 | 1200 | 0.2641          | 0.3013 | 0.0677 |
+| 0.2389        | 2.0651 | 1300 | 0.2585          | 0.2911 | 0.0652 |
+| 0.2457        | 2.2240 | 1400 | 0.2715          | 0.2923 | 0.0669 |
+| 0.2602        | 2.3828 | 1500 | 0.2530          | 0.2763 | 0.0622 |
+| 0.1614        | 2.5417 | 1600 | 0.2402          | 0.2651 | 0.0598 |
+| 0.2206        | 2.7006 | 1700 | 0.2422          | 0.2807 | 0.0621 |
+| 0.217         | 2.8594 | 1800 | 0.2356          | 0.2720 | 0.0598 |
+| 0.093         | 3.0183 | 1900 | 0.2451          | 0.2628 | 0.0597 |
+| 0.1327        | 3.1771 | 2000 | 0.2514          | 0.2710 | 0.0606 |
+| 0.0989        | 3.3360 | 2100 | 0.2552          | 0.2754 | 0.0626 |
+| 0.1772        | 3.4948 | 2200 | 0.2400          | 0.2691 | 0.0599 |
+| 0.095         | 3.6537 | 2300 | 0.2423          | 0.2499 | 0.0568 |
+| 0.1408        | 3.8125 | 2400 | 0.2392          | 0.2546 | 0.0591 |
+| 0.1694        | 3.9714 | 2500 | 0.2372          | 0.2483 | 0.0564 |
+| 0.0971        | 4.1303 | 2600 | 0.2224          | 0.2305 | 0.0512 |
+| 0.1006        | 4.2891 | 2700 | 0.2128          | 0.2308 | 0.0516 |
+| 0.1041        | 4.4480 | 2800 | 0.2229          | 0.2299 | 0.0518 |
+| 0.1004        | 4.6068 | 2900 | 0.2282          | 0.2411 | 0.0536 |
+| 0.0656        | 4.7657 | 3000 | 0.2302          | 0.2328 | 0.0520 |
+| 0.101         | 4.9245 | 3100 | 0.2150          | 0.2229 | 0.0507 |
+| 0.082         | 5.0834 | 3200 | 0.2314          | 0.2380 | 0.0541 |
+| 0.0698        | 5.2423 | 3300 | 0.2291          | 0.2264 | 0.0512 |
+| 0.0785        | 5.4011 | 3400 | 0.2198          | 0.2270 | 0.0505 |
+| 0.0595        | 5.5600 | 3500 | 0.2153          | 0.2213 | 0.0490 |
+| 0.0882        | 5.7188 | 3600 | 0.2154          | 0.2174 | 0.0487 |
+| 0.0763        | 5.8777 | 3700 | 0.2245          | 0.2178 | 0.0484 |
+| 0.0937        | 6.0365 | 3800 | 0.2250          | 0.2200 | 0.0494 |
+| 0.0664        | 6.1954 | 3900 | 0.2147          | 0.2112 | 0.0473 |
+| 0.0665        | 6.3542 | 4000 | 0.2122          | 0.2069 | 0.0464 |
+| 0.0876        | 6.5131 | 4100 | 0.2173          | 0.2117 | 0.0476 |
+| 0.0653        | 6.6720 | 4200 | 0.2088          | 0.2132 | 0.0474 |
+| 0.0863        | 6.8308 | 4300 | 0.2181          | 0.2085 | 0.0470 |
+| 0.088         | 6.9897 | 4400 | 0.2058          | 0.2049 | 0.0456 |
+| 0.0721        | 7.1485 | 4500 | 0.2155          | 0.2048 | 0.0456 |
+| 0.0474        | 7.3074 | 4600 | 0.2104          | 0.1992 | 0.0443 |
+| 0.1084        | 7.4662 | 4700 | 0.2136          | 0.1972 | 0.0444 |
+| 0.0695        | 7.6251 | 4800 | 0.2049          | 0.1922 | 0.0426 |
+| 0.0463        | 7.7840 | 4900 | 0.2117          | 0.1900 | 0.0420 |
+| 0.0485        | 7.9428 | 5000 | 0.2063          | 0.1886 | 0.0420 |
+| 0.0401        | 8.1017 | 5100 | 0.2031          | 0.1871 | 0.0414 |
+| 0.0355        | 8.2605 | 5200 | 0.2074          | 0.1886 | 0.0421 |
+| 0.0543        | 8.4194 | 5300 | 0.2140          | 0.1878 | 0.0421 |
+| 0.0364        | 8.5782 | 5400 | 0.2104          | 0.1869 | 0.0418 |
+| 0.0334        | 8.7371 | 5500 | 0.2021          | 0.1827 | 0.0407 |
+| 0.0588        | 8.8959 | 5600 | 0.1960          | 0.1808 | 0.0400 |
+| 0.0793        | 9.0548 | 5700 | 0.1980          | 0.1793 | 0.0394 |
+| 0.0597        | 9.2137 | 5800 | 0.1992          | 0.1774 | 0.0388 |
+| 0.0662        | 9.3725 | 5900 | 0.2035          | 0.1767 | 0.0388 |
+| 0.0352        | 9.5314 | 6000 | 0.2032          | 0.1773 | 0.0389 |
+| 0.0391        | 9.6902 | 6100 | 0.2041          | 0.1767 | 0.0387 |
+| 0.0357        | 9.8491 | 6200 | 0.2040          | 0.1759 | 0.0387 |
+### Framework versions
+- Transformers 4.42.0.dev0
+- Pytorch 2.3.1+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1