Update README.md

README.md

@@ -1,24 +1,34 @@
----
-license: apache-2.0
-datasets:
-- dair-ai/emotion
-language:
-- en
-metrics:
-- accuracy
-- f1
-- precision
-- recall
-base_model:
-- albert/albert-large-v2
-pipeline_tag: text-classification
----
+---
+license: apache-2.0
+datasets:
+- dair-ai/emotion
+language:
+- en
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+base_model:
+- albert/albert-large-v2
+pipeline_tag: text-classification
+---
 # Sentiment classification using Albert-large-v2
 
 ### Model Description
 
 This model is a fine-tuned version of the ALBERT-Large model designed for **emotion sentiment classification**. This model is capable of detecting six different emotional categories in text: **Anger**, **Disgust**, **Fear**, **Happiness**, **Sadness**, and **Surprise**. It achieves high performance on sentiment classification tasks, making it suitable for a variety of real-world applications such as emotion detection, content moderation, and sentiment analysis.
 
+## Evaluation
+
+| Metric                     | Value  |
+|----------------------------|--------|
+| **Evaluation Loss**        | 0.08795 |
+| **Evaluation Accuracy**    | 94.31%  |
+| **Evaluation F1-Score**    | 94.39%  |
+| **Evaluation Precision**   | 94.99%  |
+| **Evaluation Recall**      | 94.31%  |
+
 ## How to Get Started
 
 Use the code below to get started with the model.
@@ -64,13 +74,3 @@ print(emotion)
 - Domain-specific Text: The model may not perform well on specialized or highly technical texts.
 - Languages: The model has been fine-tuned on English-language data and may not generalize well to other languages.
 - Input Length: The model performs best with shorter text inputs. For longer, more complex texts, performance may vary.
-
-## Evaluation
-
-| Metric                     | Value  |
-|----------------------------|--------|
-| **Evaluation Loss**        | 0.08795 |
-| **Evaluation Accuracy**    | 94.31%  |
-| **Evaluation F1-Score**    | 94.39%  |
-| **Evaluation Precision**   | 94.99%  |
-| **Evaluation Recall**      | 94.31%  |