Update README.md

README.md

@@ -113,7 +113,7 @@ Recently, IBM has introduced GneissWeb; a large dataset yielding around 10 trill
         The GneissWeb ensemble filter uses the confidence score given to __label__hq for filtering documents based on an appropriately chosen threshold. The fastText model is used along with [DCLM-fastText] (https://huggingface.co/mlfoundations/fasttext-oh-eli5) and other quality annotators.
      2. Classifiers for [Science](https://huggingface.co/ibm-granite/GneissWeb.Sci_classifier), [Technology](https://huggingface.co/ibm-granite/GneissWeb.Tech_classifier), [Medical](https://huggingface.co/ibm-granite/GneissWeb.Med_classifier) and [Education](https://huggingface.co/ibm-granite/GneissWeb.Edu_classifier)
 
-        There are separate models for Science, Technology, Medical and Education. Each fastText model takes as input text and classifies whether the text categorized as for its subject (labeled as `__label__hq`) or other categories''cc'' (labeled as `__label__cc`). 
+        There are separate models for Science, Technology, Medical and Education. Each fastText model takes as input a text and classifies whether the text categorized as for its subject (labeled as `__label__hq`) or other categories''cc'' (labeled as `__label__cc`). 
         The model can be used with python (please refer to [fasttext documentation](https://fasttext.cc/docs/en/python-module.html) for details on using fasttext classifiers) 
         or with [IBM Data Prep Kit](https://github.com/IBM/data-prep-kit/) (DPK) (please refer to the [example notebook](https://github.com/IBM/data-prep-kit/blob/dev/transforms/language/gneissweb_classification/gneissweb_classification.ipynb) for using a fastText model with DPK).