Update README.md

README.md

@@ -5,7 +5,7 @@ An updated version of Standard-Based Impact Classification (SBIC) method of CSR
 ## Contents
 
 1. Labeled dataset (150 International companies, 230 CSR GRI reports, 2017-2021 period, 57k paragraphs, 360k sentences)
-2. Dataset for prediction (150 German PLC companies, 1.2k CSR reports, 2010-2021 period, 645k paragraphs) (available upon request)
+2. Dataset for prediction (150 German PLC companies, 1.2k CSR reports, 2010-2021 period, 645k paragraphs) (full dataset available upon request)
 3. Claculated text embeddings of both datasets
 4. Script to predict the labels
 
@@ -35,7 +35,7 @@ Before running the script, make sure you have the following input files in the w
 
 1. **Data Files**:  
    - labeled dataset: `labeled.csv`  
-   - dataset for prediction: `prediction.csv`  
+   - dataset for prediction: `prediction_demo.csv`  
 
 2. **Precomputed Embeddings**:  
    - labeled dataset: `embeddings_labeled.pkl`  
@@ -57,7 +57,7 @@ The script follows these main steps:
 2. **Perform Cosine Similarity Search**: Finds the most relevant reports (sentences) using `semantic_search` from `sentence-transformers`.  
 3. **Apply K-Nearest Neighbor (KNN) Algorithm**: Selects top similar reports (sentences) and aggregates predictions.  
 4. **Use Sigmoid Activation for Classification**: Applies a threshold to generate final classification outputs.  
-5. **Save Results**: Exports `df_results_0_50k.csv` containing the processed data.  
+5. **Save Results**: Exports `df_results_0_50k.csv` containing the  processed data for the first 50k of records.  
 
 ## **Output File**