Update README.md

README.md

@@ -118,7 +118,7 @@ Recently, IBM has introduced GneissWeb; a large dataset yielding around 10 trill
         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).
 
         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 [GneissWeb.Edu_classifier](https://huggingface.co/ibm-granite/GneissWeb.Edu_classifier), [GneissWeb.Tech_classifier](https://huggingface.co/ibm-granite/GneissWeb.Tech_classifier), and [GneissWeb.Sci_classifier](https://huggingface.co/ibm-granite/GneissWeb.Sci_classifier) and other quality annotators.
+        The fastText models are used together along with other quality annotators.
 
 2. [Bloom filter](https://huggingface.co/ibm-granite/GneissWeb.bloom) built on the document ids of  GneissWeb documents.  This can be used to recreat GneissWeb using the document ids from FineWeb 1.1.0 or any version of Common Crawl