Update README.md

README.md

@@ -37,12 +37,34 @@ The preprocessing steps included:
 Additionally, for fine-tuning the model for your own data, the preprocessing step involves converting new financial headlines into embeddings and feeding them into the RandomForest model.
 
 ### Model Evaluation
-The model has been evaluated using metrics such as:
-- **Accuracy**: The percentage of correctly classified headlines.
-- **F1-score**: The harmonic mean of precision and recall, providing a better measure of model performance when dealing with imbalanced data.
-- **Confusion Matrix**: Helps identify how well the model distinguishes between the different sentiment categories (positive, neutral, and negative).
 
-On the test data, the model achieves an **accuracy of X%**, with an **F1-score of X%**. The confusion matrix shows that the model performs well, with a high number of true positives for positive, neutral, and negative sentiments.
+
+On the test data, the model achieves an **accuracy of 61%**, with an **F1-score of 0.61**. Not optimal, but acceptable in terms of the simplicity and few data the model is trained on.
+
+#### Hyperparameters:
+- **Number of Estimators (n_estimators)**: 200
+- **Max Depth (max_depth)**: 20
+- **Min Samples Split (min_samples_split)**: 5
+- **Min Samples Leaf (min_samples_leaf)**: 1
+- **Random State (random_state)**: 42
+- **Max Features (max_features)**: 'sqrt' (default value for RandomForest)
+
+#### Classification Report:
+- **Precision**:
+   - Class 0: 0.66
+   - Class 1: 0.62
+   - Class 2: 0.55
+- **Recall**:
+   - Class 0: 0.52
+   - Class 1: 0.80
+   - Class 2: 0.52
+- **F1-Score**:
+   - Class 0: 0.58
+   - Class 1: 0.70
+   - Class 2: 0.54
+- **Overall Accuracy**: 0.61
+- **Macro Average**: 0.61 (Precision, Recall, F1-Score)
+- **Weighted Average**: 0.61 (Precision, Recall, F1-Score)
 
 ### Usage
 
@@ -50,17 +72,3 @@ To use the model, first install the necessary dependencies:
 
 ```bash
 pip install sentence-transformers scikit-learn
-
-```
-
-license: apache-2.0
-datasets:
-- NickyNicky/Finance_sentiment_and_topic_classification_En
-language:
-- en
-metrics:
-- accuracy
-base_model:
-- sentence-transformers/all-MiniLM-L6-v2
-pipeline_tag: text-classification
----