End of training
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README.md
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---
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license: apache-2.0
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base_model: facebook/wav2vec2-xls-r-300m
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tags:
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- generated_from_trainer
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datasets:
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- fleurs
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metrics:
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- wer
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model-index:
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- name: wav2vec2-base-mk
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: fleurs
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type: fleurs
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config: mk_mk
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split: test
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args: mk_mk
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metrics:
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- name: Wer
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type: wer
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value: 0.14327357528057136
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# wav2vec2-base-mk
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the fleurs dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1589
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- Wer: 0.1433
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0003
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 16
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- num_epochs: 10
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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|:-------------:|:-----:|:----:|:---------------:|:------:|
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| 3.609 | 2.33 | 400 | 0.3751 | 0.4184 |
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| 0.232 | 4.65 | 800 | 0.1694 | 0.1960 |
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| 0.0773 | 6.98 | 1200 | 0.1630 | 0.1598 |
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| 0.0407 | 9.3 | 1600 | 0.1589 | 0.1433 |
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### Framework versions
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- Transformers 4.35.2
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- Pytorch 2.1.0+cu121
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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model.safetensors
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runs/Jan16_18-33-14_4e3ada8f546b/events.out.tfevents.1705430182.4e3ada8f546b.14336.0
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