Update README.md

Biodiversity Commitment Classifier
Model Overview
This binary text classification model identifies and distinguishes biodiversity commitments in corporate sustainability reports. It classifies paragraphs as either:

Commitment: The company pledges specific actions to improve biodiversity or reduce negative environmental impacts
Non-commitment: General statements, observations, or non-actionable content related to biodiversity

Model Architecture
Built on ClimateBERT, a DistilRoBERTa-based model pre-trained on climate-related text, this classifier was fine-tuned specifically for biodiversity commitment detection in corporate disclosures.
Training Data
The model was trained on a curated dataset of 2,000 manually annotated paragraphs extracted from sustainability reports of Fortune Global 500 companies, ensuring high-quality labels and real-world applicability to corporate ESG analysis.
Performance Metrics
Evaluated using 5-fold cross-validation:
MetricScoreWeighted F10.928Weighted Precision0.930Weighted Recall0.929AUC-ROC0.976

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+Biodiversity Commitment Classifier
+Model Overview
+This binary text classification model identifies and distinguishes biodiversity commitments in corporate sustainability reports. It classifies paragraphs as either:
+
+Commitment: The company pledges specific actions to improve biodiversity or reduce negative environmental impacts
+Non-commitment: General statements, observations, or non-actionable content related to biodiversity
+
+Model Architecture
+Built on ClimateBERT, a DistilRoBERTa-based model pre-trained on climate-related text, this classifier was fine-tuned specifically for biodiversity commitment detection in corporate disclosures.
+Training Data
+The model was trained on a curated dataset of 2,000 manually annotated paragraphs extracted from sustainability reports of Fortune Global 500 companies, ensuring high-quality labels and real-world applicability to corporate ESG analysis.
+Performance Metrics
+Evaluated using 5-fold cross-validation:
+MetricScoreWeighted F10.928Weighted Precision0.930Weighted Recall0.929AUC-ROC0.976