badrex
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xlsr-polish

@@ -1,199 +1,144 @@
 ---
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 ---
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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-polish
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_17_0
+      type: common_voice_17_0
+      config: pl
+      split: validation
+      args: pl
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.1443174034459139
 ---
+<!-- 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/v7cepqow)
+# xlsr-polish
+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: 0.1686
+- Wer: 0.1443
+- Cer: 0.0313
+## 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    |
+|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
+| 4.7158        | 0.1543 | 100  | 4.0453          | 1.0    | 1.0    |
+| 3.3469        | 0.3086 | 200  | 3.2544          | 1.0    | 1.0    |
+| 2.9194        | 0.4630 | 300  | 2.7288          | 0.9985 | 0.8650 |
+| 0.921         | 0.6173 | 400  | 0.5673          | 0.5449 | 0.1303 |
+| 0.8196        | 0.7716 | 500  | 0.4311          | 0.4439 | 0.1025 |
+| 0.7248        | 0.9259 | 600  | 0.3672          | 0.3894 | 0.0875 |
+| 0.1727        | 1.0802 | 700  | 0.3141          | 0.3363 | 0.0739 |
+| 0.1807        | 1.2346 | 800  | 0.3075          | 0.3463 | 0.0758 |
+| 0.1683        | 1.3889 | 900  | 0.2969          | 0.3217 | 0.0707 |
+| 0.1616        | 1.5432 | 1000 | 0.2650          | 0.3045 | 0.0675 |
+| 0.1569        | 1.6975 | 1100 | 0.2718          | 0.2912 | 0.0658 |
+| 0.1185        | 1.8519 | 1200 | 0.2647          | 0.3139 | 0.0672 |
+| 0.1101        | 2.0062 | 1300 | 0.2476          | 0.2659 | 0.0576 |
+| 0.1296        | 2.1605 | 1400 | 0.2493          | 0.2704 | 0.0590 |
+| 0.0829        | 2.3148 | 1500 | 0.2299          | 0.2614 | 0.0576 |
+| 0.0881        | 2.4691 | 1600 | 0.2434          | 0.2670 | 0.0601 |
+| 0.125         | 2.6235 | 1700 | 0.2318          | 0.2745 | 0.0570 |
+| 0.1227        | 2.7778 | 1800 | 0.2245          | 0.2527 | 0.0542 |
+| 0.1128        | 2.9321 | 1900 | 0.2293          | 0.2600 | 0.0562 |
+| 0.079         | 3.0864 | 2000 | 0.2227          | 0.2511 | 0.0530 |
+| 0.0906        | 3.2407 | 2100 | 0.2289          | 0.2331 | 0.0515 |
+| 0.09          | 3.3951 | 2200 | 0.2196          | 0.2486 | 0.0528 |
+| 0.1113        | 3.5494 | 2300 | 0.2230          | 0.2392 | 0.0539 |
+| 0.0867        | 3.7037 | 2400 | 0.2155          | 0.2237 | 0.0492 |
+| 0.097         | 3.8580 | 2500 | 0.2120          | 0.2261 | 0.0493 |
+| 0.0659        | 4.0123 | 2600 | 0.2073          | 0.2216 | 0.0493 |
+| 0.0796        | 4.1667 | 2700 | 0.2135          | 0.2181 | 0.0468 |
+| 0.0601        | 4.3210 | 2800 | 0.2034          | 0.2190 | 0.0480 |
+| 0.0644        | 4.4753 | 2900 | 0.2115          | 0.2092 | 0.0456 |
+| 0.0772        | 4.6296 | 3000 | 0.1986          | 0.2127 | 0.0461 |
+| 0.066         | 4.7840 | 3100 | 0.1985          | 0.2027 | 0.0447 |
+| 0.0633        | 4.9383 | 3200 | 0.2094          | 0.2115 | 0.0456 |
+| 0.0579        | 5.0926 | 3300 | 0.2058          | 0.2169 | 0.0460 |
+| 0.0709        | 5.2469 | 3400 | 0.1976          | 0.1973 | 0.0428 |
+| 0.0405        | 5.4012 | 3500 | 0.2001          | 0.1965 | 0.0424 |
+| 0.0515        | 5.5556 | 3600 | 0.2035          | 0.2014 | 0.0438 |
+| 0.0785        | 5.7099 | 3700 | 0.1864          | 0.1928 | 0.0412 |
+| 0.0514        | 5.8642 | 3800 | 0.1850          | 0.1858 | 0.0397 |
+| 0.0355        | 6.0185 | 3900 | 0.1903          | 0.1837 | 0.0399 |
+| 0.0621        | 6.1728 | 4000 | 0.1881          | 0.1798 | 0.0392 |
+| 0.0525        | 6.3272 | 4100 | 0.1852          | 0.1881 | 0.0403 |
+| 0.0497        | 6.4815 | 4200 | 0.1855          | 0.1770 | 0.0387 |
+| 0.0362        | 6.6358 | 4300 | 0.1945          | 0.1899 | 0.0400 |
+| 0.0399        | 6.7901 | 4400 | 0.1803          | 0.1742 | 0.0378 |
+| 0.0483        | 6.9444 | 4500 | 0.1777          | 0.1723 | 0.0372 |
+| 0.0293        | 7.0988 | 4600 | 0.1903          | 0.1697 | 0.0369 |
+| 0.0635        | 7.2531 | 4700 | 0.1787          | 0.1726 | 0.0365 |
+| 0.0199        | 7.4074 | 4800 | 0.1722          | 0.1682 | 0.0362 |
+| 0.0393        | 7.5617 | 4900 | 0.1918          | 0.1641 | 0.0357 |
+| 0.0357        | 7.7160 | 5000 | 0.1801          | 0.1649 | 0.0358 |
+| 0.0444        | 7.8704 | 5100 | 0.1775          | 0.1626 | 0.0353 |
+| 0.0266        | 8.0247 | 5200 | 0.1693          | 0.1592 | 0.0341 |
+| 0.0381        | 8.1790 | 5300 | 0.1794          | 0.1571 | 0.0341 |
+| 0.0308        | 8.3333 | 5400 | 0.1685          | 0.1551 | 0.0333 |
+| 0.0304        | 8.4877 | 5500 | 0.1752          | 0.1519 | 0.0330 |
+| 0.0316        | 8.6420 | 5600 | 0.1752          | 0.1507 | 0.0326 |
+| 0.0377        | 8.7963 | 5700 | 0.1671          | 0.1523 | 0.0328 |
+| 0.0588        | 8.9506 | 5800 | 0.1725          | 0.1550 | 0.0335 |
+| 0.0487        | 9.1049 | 5900 | 0.1774          | 0.1531 | 0.0332 |
+| 0.0169        | 9.2593 | 6000 | 0.1709          | 0.1470 | 0.0318 |
+| 0.0274        | 9.4136 | 6100 | 0.1778          | 0.1468 | 0.0318 |
+| 0.023         | 9.5679 | 6200 | 0.1718          | 0.1482 | 0.0322 |
+| 0.0274        | 9.7222 | 6300 | 0.1700          | 0.1451 | 0.0315 |
+| 0.0349        | 9.8765 | 6400 | 0.1686          | 0.1443 | 0.0313 |
+### Framework versions
+- Transformers 4.42.0.dev0
+- Pytorch 2.3.1+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1