Initial commit

README.md

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----
+language: en
 license: mit
----
+pipeline_tag: text-classification
+base_model: bert-base-uncased
+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.
+
+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)
+
+0.790
+
+0.473
+
+0.790
+
+6-Label (5Ps)
+
+0.836
+
+0.602
+
+0.836
+
+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={[https://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)} } 

config.json

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+{
+  "_name_or_path": "models/bert-base-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7",
+    "8": "LABEL_8",
+    "9": "LABEL_9",
+    "10": "LABEL_10",
+    "11": "LABEL_11",
+    "12": "LABEL_12",
+    "13": "LABEL_13",
+    "14": "LABEL_14",
+    "15": "LABEL_15",
+    "16": "LABEL_16",
+    "17": "LABEL_17"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
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+    "LABEL_1": 1,
+    "LABEL_10": 10,
+    "LABEL_11": 11,
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+    "LABEL_8": 8,
+    "LABEL_9": 9
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.15.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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special_tokens_map.json

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+{
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+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+  "added_tokens_decoder": {
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+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
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+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}