disham993
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electrical-ner-bert-base

Token Classification

bert-base-uncased

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disham993 commited on Dec 30, 2024

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@@ -148,13 +148,11 @@ cleaned_results = clean_and_group_entities(ner_results)
 While this model performs well in the electrical engineering domain, it is not designed for use in other domains. Additionally, it may:
-- Misclassify entities due to potential inaccuracies in the GPT-4o-mini-generated dataset.
 - Struggle with ambiguous contexts or low-confidence predictions - this is minimized with help of `clean_and_group_entities` function.
 This model is intended for research and educational purposes only, and users are encouraged to validate results before applying them to critical applications.
-Users are encouraged to validate results for critical applications.
 ## Training Infrastructure
 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).

 While this model performs well in the electrical engineering domain, it is not designed for use in other domains. Additionally, it may:
+- Misclassify entities due to potential inaccuracies in the GPT-4o-mini generated dataset.
 - Struggle with ambiguous contexts or low-confidence predictions - this is minimized with help of `clean_and_group_entities` function.
 This model is intended for research and educational purposes only, and users are encouraged to validate results before applying them to critical applications.
 ## Training Infrastructure
 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).