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quality-classifier-deberta

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deberta-v2
pytorch_model_hub_mixin
model_hub_mixin
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@@ -14,7 +14,7 @@ The model was trained using data annotated by human annotators, who considered q
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  This model is used in the [NVIDIA NeMo Curator](https://github.com/NVIDIA/NeMo-Curator) as part of the qualitative filtering module.
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  # Model Architecture
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  The model architecture is Deberta V3 Base
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- Context length is 512 tokens
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  # Training (details)
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  ## Training data:
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  - 1 million Common Crawl samples, labeled using Google Cloud’s Natural Language API: https://cloud.google.com/natural-language/docs/classifying-text
 
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  This model is used in the [NVIDIA NeMo Curator](https://github.com/NVIDIA/NeMo-Curator) as part of the qualitative filtering module.
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  # Model Architecture
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  The model architecture is Deberta V3 Base
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+ Context length is 1024 tokens
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  # Training (details)
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  ## Training data:
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  - 1 million Common Crawl samples, labeled using Google Cloud’s Natural Language API: https://cloud.google.com/natural-language/docs/classifying-text