Update README.md

README.md

@@ -36,7 +36,7 @@ widget:
         Nutrient Updated: Value 4
 
 ---
-This is a BERT sequence labeling model, is designed for Named Entity Recognition (NER) in the context of FDA nutrition labeling. It aims to identify and classify various nutritional elements from text dataproviding a structured interpretation of the content typically found on nutrition labels.
+This is a BERT sequence labeling model, is designed for Named Entity Recognition (NER) in the context of nutrition labeling. It aims to identify and classify various nutritional elements from text dataproviding a structured interpretation of the content typically found on nutrition labels.
 
 
 ## Training Data Description
@@ -56,7 +56,7 @@ The training data for the `sgarbi/bert-fda-nutrition-ner` model was thoughtfully
 - **Robustness Techniques**:
     - **Introducing Noise**: To enhance the model's ability to handle real-world, imperfect data, deliberate noise was introduced into the training set. This included:
         - **Sentence Swaps**: Random swapping of sentences within the text to promote the model's understanding of varied sentence structures.
-        - **Introducing Misspellings**: Deliberately inserting common spelling errors to train the model to recognize and correctly process misspelled words frequently encountered in real-world scenarios.
+        - **Introducing Misspellings**: Deliberately inserting common spelling errors to train the model to recognize and correctly process misspelled words frequently encountered in real-world scenarios such as inaccurate document scans.
 
 ### Relevance to the Model
 - The use of a diverse and comprehensive dataset ensures that the model is well-equipped for nutritional NER tasks.