| | --- |
| | license: mit |
| | language: |
| | - en |
| | base_model: |
| | - google-bert/bert-base-uncased |
| | pipeline_tag: text-classification |
| | datasets: custom |
| | tags: |
| | - sdg |
| | - sustainable-development-goals |
| | - impact-tech |
| | - text-classification |
| | - BERT for Startup SDG Classification |
| | --- |
| | This is a bert-base-uncased model fine-tuned to classify startup company descriptions into one of the 17 UN Sustainable Development Goals (SDGs), plus a "no-impact" category. |
| | This model was trained by Kfir Bar as part of the research paper: "Using Language Models for Classifying Startups Into the UN’s 17 Sustainable Development Goals" (2022). |
| | This repository is hosted by Alon Mannor to make the original model weights accessible to the public. |
| | |
| | Model Details |
| | Base Model: bert-base-uncased |
| | Task: Text Classification |
| | Labels: 18 (0: No Impact, 1-17: corresponding SDG) |
| | Label Mapping (id2label) The model outputs a logit for each of the 18 classes. |
| | The mapping from the index (ID) to the label name is as follows: |
| | { |
| | "0": "0: No Impact", |
| | "1": "SDG 1: No Poverty", |
| | "2": "SDG 2: Zero Hunger", |
| | "3": "SDG 3: Good Health and Well-being", |
| | "4": "SDG 4: Quality Education", |
| | "5": "SDG 5: Gender Equality", |
| | "6": "SDG 6: Clean Water and Sanitation", |
| | "7": "SDG 7: Affordable and Clean Energy", |
| | "8": "SDG 8: Decent Work and Economic Growth", |
| | "9": "SDG 9: Industry, Innovation and Infrastructure", |
| | "10": "SDG 10: Reduced Inequality", |
| | "11": "SDG 11: Sustainable Cities and Communities", |
| | "12": "SDG 12: Responsible Consumption and Production", |
| | "13": "SDG 13: Climate Action", |
| | "14": "SDG 14: Life Below Water", |
| | "15": "SDG 15: Life on Land", |
| | "16": "SDG 16: Peace and Justice Strong Institutions", |
| | "17": "SDG 17: Partnerships to achieve the Goal" |
| | } |
| |
|
| | How to Use: |
| |
|
| | You can use this model directly with the text-classification pipeline. |
| |
|
| | ````python |
| | from transformers import pipeline |
| | |
| | # Load the classifier |
| | classifier = pipeline("text-classification", model="amannor/bert-base-uncased-sdgclassifier") |
| | |
| | # Example description |
| | text = "Our company develops innovative, low-cost solar panels to bring electricity to rural communities." |
| | |
| | # Get prediction |
| | result = classifier(text) |
| | print(result) |
| | # [{'label': 'SDG 7: Affordable and Clean Energy', 'score': 0.98...}] |
| | |
| | # Example of a non-impact startup |
| | text_2 = "We are a B2B platform for optimizing advertising spend on social media." |
| | result_2 = classifier(text_2) |
| | print(result_2) |
| | # [{'label': '0: No Impact', 'score': 0.95...}] |
| | ```` |
| |
|
| | Training Data: |
| | The model was trained on a dataset of 4,247 startup descriptions (from the Gidron et al. 2023 extension) aggregated from two main sources, which were manually annotated by experts: |
| | Rainmaking (Compass): A global database of impact-focused startups. |
| | Start-up Nation Central (SNC): A database of Israeli startups, including both impact and non-impact companies. |
| | |
| | Performance |
| | The model was evaluated on a test set of 866 startups from the original paper. |
| | Task: F1-Weighted F1-Macro F1-Micro 18-Label (Full )0.7900.4730.7906-Label (5Ps)0.8360.6020.836 |
| | The performance for the 6-label task (People, Planet, Prosperity, Peace, Partnerships, No-Impact) was aggregated from the 18-label predictions. |
| |
|
| | Citation: |
| | If you use this model or its underlying research, please cite the original paper: |
| | @inproceedings{bar2022usinglm, |
| | title={Using Language Models for Classifying Startups Into the UN’s 17 Sustainable Development Goals}, |
| | author={Bar, Kfir}, |
| | booktitle={Anonymous Submission to IJCAI-22}, |
| | year={2022}, |
| | url={httpsall://[github.com/Amannor/sdg-codebase/blob/master/articles/IJCAI_2022_SDGs_Methodology.pdf](https://github.com/Amannor/sdg-codebase/blob/master/articles/IJCAI_2022_SDGs_Methodology.pdf)} |
| | } |
