Update README.md

README.md

@@ -5,7 +5,7 @@ language:
 pipeline_tag: text-classification
 ---
 # Sentiment Analysis Model for Azerbaijani Text
-This repository hosts a fine-tuned XLM-RoBERTa model for sentiment analysis on Azerbaijani text. The model is capable of classifying text into three categories: negative, neutral, and positive. This README provides guidelines on how to setup and use the model for your own sentiment analysis tasks.
+This repository hosts a fine-tuned XLM-RoBERTa model for sentiment analysis on Azerbaijani text. The model is capable of classifying text into three categories: negative, neutral, and positive.
 
 ## Model Description
 The model is based on `xlm-roberta-base`, which has been fine-tuned on a diverse dataset of Azerbaijani text samples. It is designed to understand the sentiment expressed in texts and classify them accordingly.
@@ -66,30 +66,6 @@ The model outputs a label for each prediction, corresponding to one of the langu
 | 2     | positive |
 
 
-This mapping is utilized to decode the model's predictions into understandable language names, facilitating the interpretation of results for further processing or analysis.
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-
-Training Performance
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-The model was trained over three epochs, showing consistent improvement in accuracy and loss:
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-    Epoch 1: Training Loss: 0.0127, Validation Loss: 0.0174, Accuracy: 0.9966, F1 Score: 0.9966
-    Epoch 2: Training Loss: 0.0149, Validation Loss: 0.0141, Accuracy: 0.9973, F1 Score: 0.9973
-    Epoch 3: Training Loss: 0.0001, Validation Loss: 0.0109, Accuracy: 0.9984, F1 Score: 0.9984
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-Test Results
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-The model achieved the following results on the test set:
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-    Loss: 0.0133
-    Accuracy: 0.9975
-    F1 Score: 0.9975
-    Precision: 0.9975
-    Recall: 0.9975
-    Evaluation Time: 17.5 seconds
-    Samples per Second: 599.685
-    Steps per Second: 9.424
-
 
 License