Instructions to use peter2000/laya-vulnerability-groups with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use peter2000/laya-vulnerability-groups with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="peter2000/laya-vulnerability-groups")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("peter2000/laya-vulnerability-groups", device_map="auto") - Laya
How to use peter2000/laya-vulnerability-groups with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 1,704 Bytes
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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 (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).
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")` and `agent.predict(state, questions)`.
## Test-set metrics (held-out 95 rows, threshold 0.5)
| metric | value |
|---|---|
| macro-F1 | 0.6544 |
| micro-F1 | 0.6694 |
| ECE | 0.0263 |
| subset accuracy | 0.4737 |
Per-label F1:
| label | F1 |
|---|---|
| Agricultural communities | 0.9565 |
| Coastal communities | 0.5000 |
| 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 | 0.5714 |
| Older persons | 0.8889 |
| Other | 0.0000 |
| Persons living in poverty | 0.3333 |
| Persons with disabilities | 1.0000 |
| Persons with pre-existing health conditions | 0.8000 |
| Residents of drought-prone regions | 0.5714 |
| Rural populations | 0.8000 |
| Sexual minorities (LGBTQI+) | 0.3333 |
| Urban populations | 0.5714 |
| Women and other genders | 0.8649 |
|