Update README.md

README.md

@@ -35,7 +35,7 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-The model is a binary text classifier based on [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) and fine-tuned on text sourced from national climate policy documents.
+The model is a multi-class text classifier based on [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) and fine-tuned on text sourced from national climate policy documents.
 
 ## Intended uses & limitations
 
@@ -61,8 +61,10 @@ The pre-processing operations used to produce the final training dataset were as
 3. If 'context_translated' is available and the 'language' is not English, 'context' is replaced with 'context_translated'. 
 4. The dataset is "exploded" - i.e., the text samples in the 'context' column, which are lists, are converted into separate rows - and labels are merged to align with the associated samples.
 5. The 'match_onanswer' and 'answerWordcount' are used conditionally to select high quality samples (prefers high % of word matches in 'match_onanswer', but will take lower if there is a high 'answerWordcount')
-6. Data is then augmented using sentence shuffle from the ```albumentations``` library and NLP-based insertions using ```nlpaug```.
-
+6. Data is then augmented using sentence shuffle from the ```albumentations``` library and NLP-based insertions using ```nlpaug```. This is done to increase the number of training samples available for the Net-Zero class from 62 to 124. The end result is a almost equal sample per class breakdown of:
+> - 'NET-ZERO': 124
+> - 'NEGATIVE': 126
+> - 'TARGET_FREE': 125
 
 ## Training procedure