README.md

sbic-method2

An updated version of Standard-Based Impact Classification (SBIC) method of CSR report analysis in accordance with GRI framework

Here's a README section with instructions on how to run the code.

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Multilabel Classification Step

This code performs report similarity search using cosine similarity, K-Nearest Neighbor (KNN) algorithm, and Sigmoid activation function to classify reports based on embeddings.

Prerequisites

Ensure you have the following installed before running the script:

• Python 3.8+
• Required Python libraries (install using the command below)

pip install numpy pandas torch sentence-transformers scikit-learn

Input Files

Before running the script, make sure you have the following input files in the working directory:

1. Patent Data Files:

   • embeddings_labeled.csv
   • embeddings_prediction.csv

2. Precomputed Embeddings:

   • labeled dataset: embeddings_labeled.pkl
   • dataset for prediction: embeddings_prediction.pkl

Running the Script

Run the script using the following command:

python script.py

Processing Steps

The script follows these main steps:

1. Load Data & Pretrained Embeddings
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.

Output File

The processed results will be saved in:

• df_results_0_50k.csv

Execution Time

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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license: gpl-3.0