Update README.md

README.md

@@ -29,8 +29,9 @@ The model was trained on a mixture of 27 tasks and 310 classes that have been re
 'wikitoxic_toxicaggregated', 'wikitoxic_obscene', 'wikitoxic_threat', 'wikitoxic_insult', 'wikitoxic_identityhate', 
 'hateoffensive', 'hatexplain', 'biasframes_offensive', 'biasframes_sex', 'biasframes_intent',
 'agnews', 'yahootopics',
-'trueteacher', 'spam', 'wellformedquery'
-2.   Five NLI datasets with ~885k texts: "mnli", "anli", "fever", "wanli", "ling"
+'trueteacher', 'spam', 'wellformedquery'.
+See details on each dataset here: https://docs.google.com/spreadsheets/d/1Z18tMh02IiWgh6o8pfoMiI_LH4IXpr78wd_nmNd5FaE/edit?usp=sharing
+3.   Five NLI datasets with ~885k texts: "mnli", "anli", "fever", "wanli", "ling"
 
 Note that compared to other NLI models, this model predicts two classes (`entailment` vs. `not_entailment`)
 as opposed to three classes (entailment/neutral/contradiction)
@@ -59,8 +60,8 @@ Please consult the original DeBERTa paper and the papers for the different datas
 The base model (DeBERTa-v3) is published under the MIT license.
 The datasets the model was fine-tuned on are published under a diverse set of licenses.
 The following spreadsheet provides an overview of the non-NLI datasets used for fine-tuning.
-The spreadsheets contains information on licenses, the underlying papers etc. 
-https://docs.google.com/spreadsheets/d/1Z18tMh02IiWgh6o8pfoMiI_LH4IXpr78wd_nmNd5FaE/edit?usp=sharing
+The spreadsheets contains information on licenses, the underlying papers etc.: https://docs.google.com/spreadsheets/d/1Z18tMh02IiWgh6o8pfoMiI_LH4IXpr78wd_nmNd5FaE/edit?usp=sharing
+
 In addition, the model was also trained on the following NLI datasets: MNLI, ANLI, WANLI, LING-NLI, FEVER-NLI.
 
 ## Citation