ClimateBERT_GPROP_Detector

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🌿 Green Shareholder Proposal Detector

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A fine-tuned BERT-based language model to detect "greenness" within shareholder proposal.

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📋 Model Summary

Shareholder resolutions are often terse and semantically ambiguous when read in isolation. Consider a proposal requesting a report on water risk management — this may refer to environmental water stress (a climate risk) or to the human right to water access (a social issue). Such overlaps are pervasive in ESG discourse, where the same terminology routinely spans environmental, social, and governance dimensions.

This model is a fine-tuned version of ClimateBERT, specifically engineered to classify shareholder proposals as green (climate/environmental) or non-green. It is trained to resolve precisely this kind of ambiguity: rather than surface-matching sustainability keywords, it learns to identify the underlying environmental intent of a proposal from its full contextual framing.

As a result, the model is robust against false positives induced by generic ESG buzzwords — terms such as neutrality, waste, or water that frequently appear across non-environmental proposals — and maintains high precision in mixed-ESG contexts where environmental and social/governance themes co-occur.

🎯 Designed for: Extracting environmental signal from noisy, multi-topic ESG disclosures.

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🚀 Usage

⚡ Quick Start

Install dependencies first:

pip install transformers torch

Then run the following:

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
from transformers.pipelines.pt_utils import KeyDataset
import datasets
from tqdm.auto import tqdm

# ── Model ──────────────────────────────────────────────────────────────────────
model_name = "Jidi1997/ClimateBERT_GPROP_Detector"

model     = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, model_max_length=512)
pipe      = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0) # change to device=-1 if only CPU is available

# ── Data ───────────────────────────────────────────────────────────────────────
# Option A: Load your own dataset from a local CSV / JSON file
#   dataset = datasets.load_dataset("csv", data_files="your_proposals.csv", split="train")

# Option B: Construct proposals inline using the recommended input format
#   Each entry should follow the structure below for best performance:
#   "A(An) {sponsor_type}-type sponsor has filed a shareholder proposal to a(an)
#    {sic2_des}-sector company. This proposal requests: {resolution}.
#    It falls under a broader agenda class that may include items not directly
#    relevant to this specific proposal: {AgendaCodeInformation}"

dataset = datasets.Dataset.from_dict({"text": [
    # Replace with your own proposals following the recommended input format above
    """A(An) institutional-type sponsor has filed a shareholder proposal to a(an)
    energy-sector company. This proposal requests: the company to issue a report
    on its greenhouse gas emissions reduction targets.
    It falls under a broader agenda class: "..."""
]})

# ── Inference ──────────────────────────────────────────────────────────────────
# label='yes' → Green proposal (Label 1)
# label='no'  → Non-green proposal (Label 0)
for out in tqdm(pipe(KeyDataset(dataset, "text"), padding=True, truncation=True)):
    print(out)

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📌 Recommended Input Format

To address ambiguity in raw proposal text, we can enhance the model's input with structured proposal- and firm-level context, like the training data format:

"A(An) {sponsor_type}-type sponsor has filed a shareholder proposal to a(an)
{sic2_des}-sector company. This proposal requests: {resolution}.
It falls under a broader agenda class that may include items not directly
relevant to this specific proposal: {AgendaCodeInformation}"

💡 Tip: The {AgendaCodeInformation} field is optional but including it generally improves prediction confidence, as it provides additional categorical context into brief resolution context.

📦 Training Data

The model was fine-tuned on a custom stratified dataset of 1,500 manually curated ISS shareholder proposals. The dataset underwent rule-based correction to exclude purely social/governance and blend proposals.

📂 For full details on data sampling, text construction, and labeling rules, please refer to the gprop_training_dataset.

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⚙️ Training Procedure

🔧 Hyperparameters

📈 Training Results

The model weights from Epoch 8 (checkpoint-600) were selected as the best performing checkpoint based on the validation F1 score.

⭐ Best checkpoint selected at Epoch 8 — highest validation F1 of 0.9806

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📚 Citation

If you use this model in your research, please cite the associated working paper: (Forthcoming)

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Built on top of ClimateBERT · Trained with 🤗 Hugging Face Transformers

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