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@@ -148,13 +148,11 @@ cleaned_results = clean_and_group_entities(ner_results)
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  While this model performs well in the electrical engineering domain, it is not designed for use in other domains. Additionally, it may:
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- - Misclassify entities due to potential inaccuracies in the GPT-4o-mini-generated dataset.
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  - Struggle with ambiguous contexts or low-confidence predictions - this is minimized with help of `clean_and_group_entities` function.
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  This model is intended for research and educational purposes only, and users are encouraged to validate results before applying them to critical applications.
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- Users are encouraged to validate results for critical applications.
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  ## Training Infrastructure
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  For a complete guide covering the entire process - from data tokenization to pushing the model to the Hugging Face Hub - please refer to the [GitHub repository](https://github.com/di37/ner-electrical-finetuning).
 
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  While this model performs well in the electrical engineering domain, it is not designed for use in other domains. Additionally, it may:
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+ - Misclassify entities due to potential inaccuracies in the GPT-4o-mini generated dataset.
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  - Struggle with ambiguous contexts or low-confidence predictions - this is minimized with help of `clean_and_group_entities` function.
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  This model is intended for research and educational purposes only, and users are encouraged to validate results before applying them to critical applications.
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  ## Training Infrastructure
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  For a complete guide covering the entire process - from data tokenization to pushing the model to the Hugging Face Hub - please refer to the [GitHub repository](https://github.com/di37/ner-electrical-finetuning).