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README.md

@@ -54,9 +54,18 @@ It achieves the following results on the evaluation set:
 
 The distilbert-finetuned-ner model is designed for Named Entity Recognition (NER) tasks. It is based on the DistilBERT architecture, which is a smaller, faster, and lighter version of BERT. DistilBERT retains 97% of BERT's language understanding while being 60% faster and 40% smaller, making it efficient for deployment in production systems.
 
-## Intended uses & limitations
+## Intended Uses & Limitations
+
+### Intended Uses
+- Named Entity Recognition (NER): Extracting entities such as names, locations, organizations, and miscellaneous entities from text.
+- Information Extraction: Automatically identifying and classifying key information in documents.
+- Text Preprocessing: Enhancing text preprocessing for downstream tasks like sentiment analysis and text summarization.
+
+### limitations
+- Domain Specificity: The model is trained on the CoNLL-2003 dataset, which primarily consists of newswire data. Performance may degrade on text from different domains.
+- Language Limitation: This model is trained on English text. It may not perform well on text in other languages.
+- Precision in Complex Sentences: While the model performs well on standard sentences, complex sentence structures or ambiguous contexts might pose challenges.
 
-More information needed
 
 ## Training and evaluation data