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--- |
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license: gpl-3.0 |
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language: |
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- en |
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pipeline_tag: text-classification |
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tags: |
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- sbic |
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- csr |
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datasets: |
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- ia-nechaev/sbic-method2 |
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--- |
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# sbic-method2 |
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An updated version of **Standard-Based Impact Classification (SBIC) method** of CSR report analysis in accordance with GRI framework |
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## Contents |
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1. Labeled dataset (150 International companies, 230 CSR GRI reports, 2017-2021 period, 57k paragraphs, 360k sentences) |
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2. Dataset for prediction (150 German PLC companies, 1.2k CSR reports, 2010-2021 period, 645k paragraphs) (full dataset available upon request) |
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3. Claculated text embeddings of both datasets |
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4. Script to predict the labels |
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Instructions on how to run the code below. |
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--- |
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# **Multilabel Classification Steps** |
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This code performs report similarity search using **cosine similarity**, **K-Nearest Neighbor (KNN) algorithm**, and **Sigmoid activation function** to classify reports based on embeddings. |
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## **Prerequisites** |
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Ensure you have the following installed before running the script: |
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- Python 3.8+ |
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- Required Python libraries (install using the command below) |
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```bash |
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pip install numpy pandas torch sentence-transformers scikit-learn |
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``` |
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## **Input Files** |
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Before running the script, make sure you have the following input files in the working directory: |
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1. **Data Files**: |
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- labeled dataset: `labeled.csv` |
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- dataset for prediction: `prediction_demo.csv` |
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2. **Precomputed Embeddings**: |
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- labeled dataset: `embeddings_labeled.pkl` |
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- dataset for prediction: `embeddings_prediction.pkl` |
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## **Running the Script** |
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Run the script using the following command: |
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```bash |
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python script.py |
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``` |
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## **Processing Steps** |
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The script follows these main steps: |
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1. **Load Data & Pretrained Embeddings** |
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2. **Perform Cosine Similarity Search**: Finds the most relevant reports (sentences) using `semantic_search` from `sentence-transformers`. |
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3. **Apply K-Nearest Neighbor (KNN) Algorithm**: Selects top similar reports (sentences) and aggregates predictions. |
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4. **Use Sigmoid Activation for Classification**: Applies a threshold to generate final classification outputs. |
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5. **Save Results**: Exports `df_results_0_50k.csv` containing the processed data for the first 50k of records. |
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## **Output File** |
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The processed results will be saved in: `df_results_0_50k.csv` |
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## **Execution Time** |
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Execution time depends on the number of test samples and system resources. The script prints the total processing time upon completion. |
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