Update README.md

README.md

@@ -10,11 +10,10 @@ metrics:
 model-index:
 - name: electra-base-emotion-Tweet_About_Disaster_Or_Not
   results: []
+language:
+- en
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # electra-base-emotion-Tweet_About_Disaster_Or_Not
 
 This model is a fine-tuned version of [bhadresh-savani/electra-base-emotion](https://huggingface.co/bhadresh-savani/electra-base-emotion) on the None dataset.
@@ -27,15 +26,28 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This is a binary classification model to determine if tweet input samples are about a disaster or not.
+
+For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Binary%20Classification/Transformer%20Comparison/Is%20This%20Tweet%20Referring%20to%20a%20Disaster%20or%20Not%3F%20-%20ELECTRA.ipynb
+
+### Associated Projects
+This project is part of a comparison of multiple transformers. The others can be found at the following links:
+
+- https://huggingface.co/DunnBC22/roberta-base-Tweet_About_Disaster_Or_Not
+- https://huggingface.co/DunnBC22/deberta-v3-small-Tweet_About_Disaster_Or_Not
+- https://huggingface.co/DunnBC22/albert-base-v2-Tweet_About_Disaster_Or_Not
+- https://huggingface.co/DunnBC22/ernie-2.0-base-en-Tweet_About_Disaster_Or_Not
+- https://huggingface.co/DunnBC22/distilbert-base-uncased-Tweet_About_Disaster_Or_Not
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended to demonstrate my ability to solve a complex problem using technology.
+
+The main limitation is the quality of the data source.
 
 ## Training and evaluation data
 
-More information needed
+Dataset Source: https://www.kaggle.com/datasets/vstepanenko/disaster-tweets
 
 ## Training procedure
 
@@ -66,4 +78,4 @@ The following hyperparameters were used during training:
 - Transformers 4.26.1
 - Pytorch 1.13.1
 - Datasets 2.9.0
-- Tokenizers 0.12.1
+- Tokenizers 0.12.1