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xls-r-300-cv17-polish-adap-ru

@@ -1,199 +1,111 @@
 ---
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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: xls-r-300-cv17-polish-adap-ru
+  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: 0.29855366457663735
 ---
+<!-- 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/x0030ten)
+# xls-r-300-cv17-polish-adap-ru
+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: 0.4087
+- Wer: 0.2986
+- Cer: 0.0652
+## 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: 50
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
+| 3.2673        | 1.6   | 100  | 3.3121          | 1.0    | 1.0    |
+| 1.2344        | 3.2   | 200  | 1.1417          | 0.8846 | 0.2502 |
+| 0.4279        | 4.8   | 300  | 0.4485          | 0.4848 | 0.1082 |
+| 0.2415        | 6.4   | 400  | 0.3752          | 0.3971 | 0.0871 |
+| 0.2634        | 8.0   | 500  | 0.4058          | 0.4148 | 0.0927 |
+| 0.1683        | 9.6   | 600  | 0.4079          | 0.3906 | 0.0887 |
+| 0.1356        | 11.2  | 700  | 0.4017          | 0.3927 | 0.0872 |
+| 0.0887        | 12.8  | 800  | 0.4094          | 0.3867 | 0.0874 |
+| 0.1529        | 14.4  | 900  | 0.4055          | 0.3728 | 0.0843 |
+| 0.1206        | 16.0  | 1000 | 0.4030          | 0.3709 | 0.0824 |
+| 0.0573        | 17.6  | 1100 | 0.4370          | 0.3787 | 0.0841 |
+| 0.073         | 19.2  | 1200 | 0.4157          | 0.3653 | 0.0819 |
+| 0.0498        | 20.8  | 1300 | 0.4235          | 0.3637 | 0.0811 |
+| 0.0987        | 22.4  | 1400 | 0.4153          | 0.3526 | 0.0786 |
+| 0.0791        | 24.0  | 1500 | 0.4239          | 0.3557 | 0.0802 |
+| 0.0698        | 25.6  | 1600 | 0.4253          | 0.3473 | 0.0779 |
+| 0.0745        | 27.2  | 1700 | 0.4092          | 0.3518 | 0.0784 |
+| 0.0689        | 28.8  | 1800 | 0.4326          | 0.3433 | 0.0764 |
+| 0.059         | 30.4  | 1900 | 0.4207          | 0.3342 | 0.0738 |
+| 0.0255        | 32.0  | 2000 | 0.4053          | 0.3272 | 0.0726 |
+| 0.0403        | 33.6  | 2100 | 0.4267          | 0.3264 | 0.0715 |
+| 0.0281        | 35.2  | 2200 | 0.4141          | 0.3250 | 0.0719 |
+| 0.0533        | 36.8  | 2300 | 0.4242          | 0.3252 | 0.0718 |
+| 0.0503        | 38.4  | 2400 | 0.4062          | 0.3147 | 0.0690 |
+| 0.0292        | 40.0  | 2500 | 0.4109          | 0.3081 | 0.0676 |
+| 0.0276        | 41.6  | 2600 | 0.3919          | 0.3044 | 0.0665 |
+| 0.0177        | 43.2  | 2700 | 0.4104          | 0.3038 | 0.0664 |
+| 0.0268        | 44.8  | 2800 | 0.4149          | 0.3040 | 0.0662 |
+| 0.0388        | 46.4  | 2900 | 0.4090          | 0.3003 | 0.0656 |
+| 0.0193        | 48.0  | 3000 | 0.4092          | 0.2994 | 0.0652 |
+| 0.0428        | 49.6  | 3100 | 0.4087          | 0.2986 | 0.0652 |
+### Framework versions
+- Transformers 4.42.0.dev0
+- Pytorch 2.3.1+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1