Update README.md

README.md

@@ -12,10 +12,10 @@ license: mit
 # Model description:  deberta-v3-large-zeroshot-v1.1-all-33
 The model is designed for zero-shot classification with the Hugging Face pipeline. 
 
-The model can do one universal task: determine whether a hypothesis is `true` or `not_true`
-given a text (also called `entailment` vs. `not_entailment`).  
+The model can do one universal classification task: determine whether a hypothesis is "true" or "not true" given a text
+(`entailment` vs. `not_entailment`).  
 This task format is based on the Natural Language Inference task (NLI).
-The task is so universal that any classification task can be reformulated into the task.
+The task is so universal that any classification task can be reformulated into this task.
 
 A detailed description of how the model was trained and how it can be used is available in this paper: [link to be added]
 
@@ -123,8 +123,8 @@ Please consult the original DeBERTa paper and the papers for the different datas
 ## License
 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://github.com/MoritzLaurer/zeroshot-classifier/blob/main/datasets_overview.csv
+The following table provides an overview of the non-NLI datasets used for fine-tuning, 
+information on licenses, the underlying papers etc.: https://github.com/MoritzLaurer/zeroshot-classifier/blob/main/datasets_overview.csv
 
 ## Citation
 If you use this model academically, please cite: