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@@ -6,9 +6,17 @@ tags:
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  metrics:
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  - bleu
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  - wer
 
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  model-index:
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  - name: Whisper Base GA-EN Speech Translation
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  results: []
 
 
 
 
 
 
 
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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
@@ -17,7 +25,7 @@ should probably proofread and complete it, then remove this comment. -->
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  # Whisper Base GA-EN Speech Translation
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  This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on an unknown dataset.
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- It achieves the following results on the evaluation set:
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  - Loss: 1.9005
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  - Bleu: 21.83
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  - Chrf: 37.13
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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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  - Transformers 4.39.3
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  - Pytorch 2.2.1+cu121
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  - Datasets 2.18.0
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- - Tokenizers 0.15.2
 
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  metrics:
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  - bleu
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  - wer
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+ - chrf
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  model-index:
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  - name: Whisper Base GA-EN Speech Translation
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  results: []
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+ datasets:
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+ - ymoslem/IWSLT2023-GA-EN
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+ - ymoslem/FLEURS-GA-EN
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+ language:
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+ - ga
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+ - en
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+ library_name: transformers
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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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  # Whisper Base GA-EN Speech Translation
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  This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on an unknown dataset.
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+ The best model (this version) is at checkpoint 1000, epoch 2.54, and it achieves the following results on the evaluation set:
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  - Loss: 1.9005
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  - Bleu: 21.83
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  - Chrf: 37.13
 
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  ## Training procedure
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+ ### Experiment
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+
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+ - Data (v1.1: IWSLT2023-GA-EN; v1.2: +FLEURS-GA-EN)
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+
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
 
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  - Transformers 4.39.3
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  - Pytorch 2.2.1+cu121
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  - Datasets 2.18.0
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+ - Tokenizers 0.15.2