badrex
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xls-r-300m-hbs-phoneme-unfrozen-batch16

@@ -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-300m-hbs-phoneme-unfrozen-batch16
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_17_0
+      type: common_voice_17_0
+      config: hsb
+      split: test
+      args: hsb
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.4111996251171509
 ---
+<!-- 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/runs/7invqf4p)
+# xls-r-300m-hbs-phoneme-unfrozen-batch16
+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: 0.7105
+- Wer: 0.4112
+- Cer: 0.0948
+## 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: 100
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    |
+|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
+| 3.6184        | 3.2258  | 100  | 3.4215          | 1.0    | 1.0    |
+| 3.2927        | 6.4516  | 200  | 3.2247          | 1.0    | 1.0    |
+| 3.2291        | 9.6774  | 300  | 3.2021          | 1.0    | 1.0000 |
+| 1.4844        | 12.9032 | 400  | 1.3507          | 0.9857 | 0.2837 |
+| 0.4136        | 16.1290 | 500  | 0.6982          | 0.6567 | 0.1608 |
+| 0.2346        | 19.3548 | 600  | 0.6496          | 0.5956 | 0.1466 |
+| 0.1401        | 22.5806 | 700  | 0.6680          | 0.5565 | 0.1314 |
+| 0.1535        | 25.8065 | 800  | 0.6597          | 0.5026 | 0.1190 |
+| 0.1165        | 29.0323 | 900  | 0.7085          | 0.5112 | 0.1224 |
+| 0.076         | 32.2581 | 1000 | 0.7359          | 0.5026 | 0.1195 |
+| 0.083         | 35.4839 | 1100 | 0.7144          | 0.4991 | 0.1205 |
+| 0.0985        | 38.7097 | 1200 | 0.6907          | 0.4756 | 0.1120 |
+| 0.052         | 41.9355 | 1300 | 0.6806          | 0.4700 | 0.1105 |
+| 0.0347        | 45.1613 | 1400 | 0.7097          | 0.4588 | 0.1091 |
+| 0.0432        | 48.3871 | 1500 | 0.7086          | 0.4649 | 0.1093 |
+| 0.0626        | 51.6129 | 1600 | 0.6947          | 0.4393 | 0.1029 |
+| 0.0474        | 54.8387 | 1700 | 0.6915          | 0.4468 | 0.1058 |
+| 0.057         | 58.0645 | 1800 | 0.7068          | 0.4358 | 0.1020 |
+| 0.0373        | 61.2903 | 1900 | 0.7140          | 0.4419 | 0.1037 |
+| 0.0994        | 64.5161 | 2000 | 0.6966          | 0.4208 | 0.0987 |
+| 0.0503        | 67.7419 | 2100 | 0.6997          | 0.4306 | 0.0988 |
+| 0.0418        | 70.9677 | 2200 | 0.7105          | 0.4353 | 0.1006 |
+| 0.036         | 74.1935 | 2300 | 0.7320          | 0.4356 | 0.1024 |
+| 0.0171        | 77.4194 | 2400 | 0.7132          | 0.4257 | 0.0994 |
+| 0.0234        | 80.6452 | 2500 | 0.7059          | 0.4171 | 0.0967 |
+| 0.0335        | 83.8710 | 2600 | 0.7449          | 0.4140 | 0.0973 |
+| 0.0288        | 87.0968 | 2700 | 0.7028          | 0.4157 | 0.0964 |
+| 0.0344        | 90.3226 | 2800 | 0.7181          | 0.4112 | 0.0960 |
+| 0.0298        | 93.5484 | 2900 | 0.7150          | 0.4105 | 0.0951 |
+| 0.0532        | 96.7742 | 3000 | 0.7164          | 0.4119 | 0.0950 |
+| 0.0058        | 100.0   | 3100 | 0.7105          | 0.4112 | 0.0948 |
+### Framework versions
+- Transformers 4.42.0.dev0
+- Pytorch 2.3.1+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1