nutella-toast commited on
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End of training

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README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Wer
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  type: wer
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- value: 1.0
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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
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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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 ml-superb-subset dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.9777
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- - Wer: 1.0
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  ## Model description
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@@ -66,17 +66,17 @@ The following hyperparameters were used during training:
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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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- | 11.645 | 1.0471 | 100 | 10.4228 | 1.0 |
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- | 3.2477 | 2.0942 | 200 | 3.1142 | 1.0 |
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- | 3.1938 | 3.1414 | 300 | 3.0785 | 1.0 |
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- | 3.3024 | 4.1885 | 400 | 3.2026 | 1.0 |
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- | 3.1523 | 5.2356 | 500 | 3.1474 | 1.0 |
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- | 3.1189 | 6.2827 | 600 | 3.0652 | 1.0 |
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- | 3.0489 | 7.3298 | 700 | 3.0268 | 1.0 |
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- | 2.9999 | 8.3770 | 800 | 2.9902 | 1.0 |
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- | 2.9698 | 9.4241 | 900 | 2.9777 | 1.0 |
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  ### Framework versions
 
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  metrics:
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  - name: Wer
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  type: wer
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+ value: 0.9968847352024922
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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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  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the ml-superb-subset dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.4697
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+ - Wer: 0.9969
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  ## Model description
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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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+ | 6.3138 | 1.0471 | 100 | 4.7003 | 1.0 |
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+ | 3.1815 | 2.0942 | 200 | 3.1195 | 1.0 |
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+ | 3.1618 | 3.1414 | 300 | 3.1440 | 1.0 |
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+ | 3.068 | 4.1885 | 400 | 3.2146 | 1.0 |
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+ | 3.0495 | 5.2356 | 500 | 3.0380 | 1.0 |
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+ | 2.9972 | 6.2827 | 600 | 2.9489 | 1.0 |
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+ | 2.6887 | 7.3298 | 700 | 2.5815 | 1.0 |
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+ | 2.2022 | 8.3770 | 800 | 1.9518 | 1.0 |
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+ | 1.6504 | 9.4241 | 900 | 1.4697 | 0.9969 |
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  ### Framework versions
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