Update README.md

README.md

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 ---
 license: apache-2.0
+language:
+- en
+tags:
+- hr
+- layoffs
+- workforce
+- employment
+- global
+- autoscientist
+base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
 ---
+
+# Global Layoffs & Workforce Interpreter
+
+**Author:** Hussein Adeiza (mabera)
+**Role:** Licensed Environmental Health Officer, Abuja Nigeria
+**Base Model:** Mixtral 8x7B
+**Fine-tuned with:** AutoScientist by Adaption Labs
+
+## Model Description
+A LoRA adapter fine-tuned to interpret real global tech layoffs data,
+producing structured analytical reasoning grounded entirely in
+directly-downloaded statistics from a real tracked dataset (layoffs.fyi).
+This submission completes all 7 categories of the AutoScientist
+Challenge 2026 Part 2.
+
+## A Note on Subject Matter
+This dataset addresses real workforce reductions affecting real
+people's livelihoods. All interpretation stays measured and analytical,
+including honest disclosure of data limitations (missing figures,
+likely reporting bias) rather than overstating confidence in the
+underlying figures.
+
+## Training Data
+- Source: layoffs.fyi tracked data, accessed via a properly-cited
+  GitHub repository, downloaded directly, 2,361 real company entries
+  (2020-2023)
+- Dataset: 5 original prompt-completion pairs, every number computed
+  via pandas directly from the raw source file, expanded via Adaptive
+  Data (no recipe modifications, full 20K+ datapoint expansion)
+- Kaggle: https://www.kaggle.com/datasets/yunusahusseinadeiza/global-layoffs-and-workforce-interpreter
+
+## Training Metrics
+- **Win rate (on dataset): 70% adapted vs 30% base model**
+- Base model: mistralai/Mixtral-8x7B-Instruct-v0.1
+- Method: LoRA, no recipe modifications
+- Dataset quality: 8.0 → 8.6 (+7.5% relative improvement, Grade B)
+- Percentile: 31.5
+- Domain classification: Data-analysis-visualization (60%) / Market-
+  analysis (20%) / Corporate-business (20%), not a clean HR match;
+  disclosed here as this submission was intended for the HR category
+
+## Key Cited Findings (from the raw downloaded source only)
+- Global tracked layoffs followed a non-monotonic trend: 81,068 in
+  2020 (COVID shock), falling to 15,823 in 2021 (recovery), then
+  rising to 161,711 in 2022 and 127,277 in the first quarter of 2023
+  alone
+- Funding raised shows almost no correlation with layoff scale
+  (r = 0.077 across 1,491 companies with complete data), challenging
+  the intuitive assumption that better-funded companies cut more
+- Post-IPO (mature, publicly-traded) companies account for 53.1% of
+  total tracked layoffs, likely reflecting company scale rather than
+  instability
+- Roughly a third of entries (31.3% missing headcount, 33.2% missing
+  percentage) lack key figures, meaning aggregate totals are
+  conservative lower bounds, not precise counts
+- The dataset's heavy US concentration (66% of the geographic total)
+  most likely reflects reporting bias in the underlying tracker toward
+  English-language tech media, not a true global distribution
+
+## Credits
+Powered by Adaptive Data — Adaption Labs
+AutoScientist Challenge 2026, Part 2 — HR Category