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xls-r-300m-hbs-ru-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-ru-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: 0.37207122774133083
 ---
+<!-- 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/dgxuea1c)
+# xls-r-300m-hbs-ru-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: 0.6191
+- Wer: 0.3721
+- Cer: 0.0853
+## 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    |
+|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
+| 3.3829        | 3.2258  | 100  | 3.3113          | 1.0    | 1.0    |
+| 3.0722        | 6.4516  | 200  | 3.0062          | 1.0    | 0.9991 |
+| 0.5001        | 9.6774  | 300  | 0.6462          | 0.6396 | 0.1553 |
+| 0.2668        | 12.9032 | 400  | 0.5761          | 0.5567 | 0.1386 |
+| 0.1468        | 16.1290 | 500  | 0.5573          | 0.4986 | 0.1192 |
+| 0.1351        | 19.3548 | 600  | 0.5716          | 0.4862 | 0.1139 |
+| 0.1263        | 22.5806 | 700  | 0.5959          | 0.4841 | 0.1178 |
+| 0.094         | 25.8065 | 800  | 0.5752          | 0.4391 | 0.1024 |
+| 0.0473        | 29.0323 | 900  | 0.6015          | 0.4445 | 0.1059 |
+| 0.0442        | 32.2581 | 1000 | 0.6266          | 0.4616 | 0.1127 |
+| 0.0727        | 35.4839 | 1100 | 0.6193          | 0.4442 | 0.1069 |
+| 0.0494        | 38.7097 | 1200 | 0.6244          | 0.4349 | 0.1023 |
+| 0.027         | 41.9355 | 1300 | 0.6457          | 0.4391 | 0.1038 |
+| 0.0277        | 45.1613 | 1400 | 0.6470          | 0.4351 | 0.1045 |
+| 0.0326        | 48.3871 | 1500 | 0.6137          | 0.4093 | 0.0986 |
+| 0.0511        | 51.6129 | 1600 | 0.6152          | 0.4182 | 0.0975 |
+| 0.0431        | 54.8387 | 1700 | 0.5967          | 0.4210 | 0.1011 |
+| 0.0749        | 58.0645 | 1800 | 0.6173          | 0.4276 | 0.1034 |
+| 0.032         | 61.2903 | 1900 | 0.6318          | 0.4201 | 0.0990 |
+| 0.0504        | 64.5161 | 2000 | 0.6174          | 0.4227 | 0.0999 |
+| 0.0308        | 67.7419 | 2100 | 0.6174          | 0.4007 | 0.0937 |
+| 0.0301        | 70.9677 | 2200 | 0.6148          | 0.3962 | 0.0923 |
+| 0.0178        | 74.1935 | 2300 | 0.6038          | 0.4044 | 0.0945 |
+| 0.018         | 77.4194 | 2400 | 0.5975          | 0.3878 | 0.0912 |
+| 0.0112        | 80.6452 | 2500 | 0.6183          | 0.3913 | 0.0927 |
+| 0.0432        | 83.8710 | 2600 | 0.6346          | 0.3845 | 0.0905 |
+| 0.0327        | 87.0968 | 2700 | 0.6327          | 0.3793 | 0.0877 |
+| 0.0254        | 90.3226 | 2800 | 0.6270          | 0.3770 | 0.0882 |
+| 0.0199        | 93.5484 | 2900 | 0.6250          | 0.3751 | 0.0868 |
+| 0.0147        | 96.7742 | 3000 | 0.6222          | 0.3709 | 0.0855 |
+| 0.0025        | 100.0   | 3100 | 0.6191          | 0.3721 | 0.0853 |
+### Framework versions
+- Transformers 4.42.0.dev0
+- Pytorch 2.3.1+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1