nvidia
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quality-classifier-deberta

pytorch_model_hub_mixin

model_hub_mixin

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sarahyurick commited on Sep 22

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Update Curator links

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@@ -11,7 +11,7 @@ This is a text classification model that can enable qualitative data annotation,
 The model was trained using data annotated by human annotators, who considered quality factors such as content accuracy, clarity, coherence, grammar, depth of information, and overall usefulness of the document.
-This model is used in the [NVIDIA NeMo Curator](https://github.com/NVIDIA/NeMo-Curator) as part of the qualitative filtering module.
 # Model Architecture
 The model architecture is Deberta V3 Base
 Context length is 1024 tokens
@@ -68,7 +68,7 @@ Medium
 # How to use in NeMo Curator
-The inference code is available on [NeMo Curator's GitHub repository](https://github.com/NVIDIA/NeMo-Curator). Check out this [example notebook](https://github.com/NVIDIA/NeMo-Curator/blob/main/tutorials/distributed_data_classification/quality-classification.ipynb) to get started.
 # How to use in transformers
 To use the quality classifier, use the following code:

 The model was trained using data annotated by human annotators, who considered quality factors such as content accuracy, clarity, coherence, grammar, depth of information, and overall usefulness of the document.
+This model is used in the [NVIDIA NeMo Curator](https://github.com/NVIDIA-NeMo/Curator) as part of the qualitative filtering module.
 # Model Architecture
 The model architecture is Deberta V3 Base
 Context length is 1024 tokens
 # How to use in NeMo Curator
+The inference code is available on [NeMo Curator's GitHub repository](https://github.com/NVIDIA-NeMo/Curator). Check out this [example notebook](https://github.com/NVIDIA-NeMo/Curator/blob/main/tutorials/text/distributed-data-classification/quality-classification.ipynb) to get started.
 # How to use in transformers
 To use the quality classifier, use the following code: