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README.md
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@@ -26,7 +26,7 @@ The fastText model is used along with [GneissWeb.Edu_classifier](https://hugging
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**Training Data**
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The model is trained on 800k documents, labeled using the [WatsonNLP hierachical categorization](https://www.ibm.com/docs/en/watsonx/saas?topic=catalog-hierarchical-categorization). Please refer to [fastText text classification tutorial](https://fasttext.cc/docs/en/python-module.html) for details.
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Training data is selected as follows
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- *Positive documents*: 400k documents randomly sampled from the documents labeled with technology category with a confidence score 0.95 and above.
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- *Negative documents*: 400k documents randomly sampled from the documents labeled with any category other than science, education, medical, and technology categories with a confidence score of 0.95 and above.
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**Training Data**
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The model is trained on 800k documents, labeled using the [WatsonNLP hierachical categorization](https://www.ibm.com/docs/en/watsonx/saas?topic=catalog-hierarchical-categorization). Please refer to [fastText text classification tutorial](https://fasttext.cc/docs/en/python-module.html) for details.
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Training data is selected as follows:
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- *Positive documents*: 400k documents randomly sampled from the documents labeled with technology category with a confidence score 0.95 and above.
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- *Negative documents*: 400k documents randomly sampled from the documents labeled with any category other than science, education, medical, and technology categories with a confidence score of 0.95 and above.
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