| --- |
| license: apache-2.0 |
| library_name: transformers |
| tags: [laya, text-classification, multi-label, climate, vulnerability, rlcd] |
| pipeline_tag: text-classification |
| --- |
| |
| # Laya fine-tuned for climate-vulnerability group detection, v2 (multi-label) |
|
|
| [convaiinnovations/laya](https://huggingface.co/convaiinnovations/laya) (421M, ModernBERT-large backbone) |
| fine-tuned with the official RLCD recipe on [GIZ/vulnerability_training_data_full](https://huggingface.co/datasets/GIZ/vulnerability_training_data_full) |
| (380 train rows x 17 binary vulnerability-group questions; 36 all-negative rows included as negatives). |
|
|
| **v2 change vs [v1](https://huggingface.co/peter2000/laya-vulnerability-groups):** the typed-question instructions now carry the |
| label into context — each binary question reads "Does this text indicate that <group> are targeted, supported, or affected? This group is specifically vulnerable to climate change. Answer true or false." |
|
|
| Each vulnerability group is asked as one binary (`noul`) typed question; all 17 are answered in a single forward pass. |
| Evaluate with `laya.load("peter2000/laya-vulnerability-groups-v2")` and `agent.predict(state, questions)`. |
|
|
| ## Test-set metrics (held-out 95 rows, threshold 0.5) |
|
|
| | metric | value | |
| |---|---| |
| | macro-F1 | 0.7038 | |
| | micro-F1 | 0.7083 | |
| | ECE | 0.0242 | |
| | subset accuracy | 0.4737 | |
|
|
| Per-label F1: |
|
|
| | label | F1 | |
| |---|---| |
| | Agricultural communities | 0.9167 | |
| | Coastal communities | 0.5714 | |
| | Ethnic, racial or other minorities | 0.6000 | |
| | Fishery communities | 0.4000 | |
| | Informal sector workers | 1.0000 | |
| | Members of indigenous and local communities | 0.9333 | |
| | Migrants and displaced persons | 0.6667 | |
| | Older persons | 0.7778 | |
| | Other | 0.0000 | |
| | Persons living in poverty | 0.6087 | |
| | Persons with disabilities | 1.0000 | |
| | Persons with pre-existing health conditions | 1.0000 | |
| | Residents of drought-prone regions | 0.8000 | |
| | Rural populations | 0.8889 | |
| | Sexual minorities (LGBTQI+) | 0.3333 | |
| | Urban populations | 0.5455 | |
| | Women and other genders | 0.9231 | |
|
|
