Update README.md

README.md

@@ -2,6 +2,7 @@
 license: apache-2.0
 task_categories:
 - text-retrieval
+- text-classification
 language:
 - en
 tags:
@@ -37,4 +38,4 @@ NAICS Classification is a fundamental component of enterprise-level GHG emission
 
 Each enterprise description (query) is encoded separately, and matched against NAICS descriptions (corpus) based on the cosine similarity of their embeddings. This methodology leverages a dual-tower architecture, wherein the first tower processes the query (enterprise text) and the second tower processes NAICS descriptions.
 
-We apply machine learning to fine-tune a pre-trained Sentence-BERT model. Zero-shot SBERT models may achieve only around 20% Top-1 accuracy on the 1000 classes sector classification task, whereas contrastive fine-tuning raises this to over 75%. Further preprocessing exceeding 77% Top-1 accuracy, such as lowercasing and URL removal, can add incremental gains, leading to state-of-the-art results.
+We apply machine learning to fine-tune a pre-trained Sentence-BERT model. Zero-shot SBERT models may achieve only around 20% Top-1 accuracy on the 1000 classes sector classification task, whereas contrastive fine-tuning raises this to over 75%. Further preprocessing exceeding 77% Top-1 accuracy, such as lowercasing and URL removal, can add incremental gains, leading to state-of-the-art results.