Update README.md

README.md

@@ -6,29 +6,30 @@ pipeline_tag: text-classification
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 # EmoBERTv2 Model
-
+This Model Card is a work in progress and will be completed in the future (dataset upload pending, etc)
 ## Model Description
 EmoBERTv2 is a emotion text classification model trained on a large dataset of english social media posts. The model is fine-tuned
 from "prajjwal1-bert-tiny" EmoBERTv2 can be used for either further fine-tuning, or for usage in real-time emotion prediction applications
 
 
 ## Datasets
-This model was trained on a diverse range of datasets including [Name of Datasets], ensuring broad coverage of various emotional expressions.
+This model was trained on the [Dataset Name] dataset, which is an aggregation of many datasets through relabling and data subsetting. The
+dataset has 9 labels: joy, sad, love, anger, disgust, surprise, neutral, fear, and worry
 
 ## Training Procedure
 EmoBERTv2 was fine-tuned from [Base Model Name] with specific hyperparameters [List Hyperparameters]. Training involved [X] epochs, using a learning rate of [Y].
 
 ## Intended Use
-This model is intended for emotion and sentiment analysis in [specific domains or general use]. It should be used as a tool for [Specify Applications].
+This model is intended for emotion classification in [specific domains or general use]. It should be used as a tool for [Specify Applications].
 
 ## Performance
-EmoBERTv2 demonstrates an accuracy of [X]% on the [Test Dataset Name]. For detailed performance metrics, refer to [Link to Performance Metrics].
+EmoBERTv2 demonstrates an accuracy of 86.17% on the [Test Dataset Name]Test set. For detailed performance metrics, refer to [Link to Performance Metrics].
 
 ## Bias and Fairness
 While efforts have been made to reduce bias, users should be aware of potential biases in the data. It is advisable to test the model in specific contexts.
 
 ## Licensing and Usage
-EmoBERTv2 is released under the [License Name] and can be freely used as outlined in the license.
+EmoBERTv2 is released under the MIT License and can be freely used as outlined in the license.
 
 ## Other Model Variations
 Additional variations of EmoBERTv2 include [List Variations]. These variations offer different trade-offs in terms of size, speed, and performance.