Update README.md

README.md

@@ -8,12 +8,12 @@ tags:
 datasets:
 - disham993/ElectricalDeviceFeedbackBalanced
 metrics:
-- epoch: 1
-- eval_f1: 0.8353275880967258
-- eval_accuracy: 0.856508875739645
-- eval_runtime: 0.4632
-- eval_samples_per_second: 2918.69
-- eval_steps_per_second: 47.493
+- epoch: 5.0
+- eval_f1: 0.8780
+- eval_accuracy: 0.8794
+- eval_runtime: 0.4649
+- eval_samples_per_second: 2908.428
+- eval_steps_per_second: 47.326
 library_name: transformers
 ---
 
@@ -49,14 +49,16 @@ The model was fine-tuned using the following hyperparameters:
 
 The following metrics were achieved during evaluation:
 
-- **F1 Score:** 0.8899
-- **Accuracy:** 0.8875
-- **eval_runtime**: 1.2105
-- **eval_samples_per_second**: 1116.881
-- **eval_steps_per_second**: 18.174
+- **F1 Score:** 0.8780
+- **Accuracy:** 0.8794
+- **eval_runtime**: 0.4649
+- **eval_samples_per_second**: 2908.428
+- **eval_steps_per_second**: 47.326
 
 ## Usage
 
+You can use this model for Sentiment Analysis of the Electrical Device Feedback as follows:
+
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline