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| # Model Card · ModelOps Research Lab baselines | |
| ## Intended purpose | |
| Research demonstration of reproducible, browser-trained multilingual academic-sentence classification. Labels: background, objective, method, result, limitation, implication. | |
| ## Model architectures | |
| Not neural networks. Four transparent lexical baselines: multinomial Naive Bayes, word TF-IDF class centroid, character trigram TF-IDF class centroid and a fixed 55/45 centroid hybrid. No pretrained weights and no API inference. | |
| ## Training / evaluation | |
| Training on the 842-row original fixed synthetic-curated training set (EN / ES / PT). Held-out assessments use 130 controlled IID examples and 54 fixed novel-pattern synthetic examples. Benchmark code is published in `engine.js` and reproduced by `benchmark_cli.js`. The release metrics are in `evaluation_results.json`. | |
| ## Scores | |
| | Model | IID Accuracy | Challenge Accuracy | Challenge Macro F1 | | |
| |---|---:|---:|---:| | |
| | NB | 100.0% | 74.1% | 73.6% | | |
| | Word centroid | 98.5% | 72.2% | 70.6% | | |
| | Char centroid | 95.4% | 70.4% | 68.4% | | |
| | Hybrid | 97.7% | 77.8% | 76.2% | | |
| No external human-labeled validation has been performed for **these** browser models. A separate archived Python classifier was once smoke-tested on PubMed; those figures are contextual only and not directly comparable. | |
| ## Known limitations and risks | |
| Controlled synthetic data, likely recurring templates, limited domain scope, demographic/fairness relevance not assessed, no calibrated probabilities, unreliable transfer to real scholarly prose. **Prohibited for grading, academic-integrity judgments, student assessment, admissions, employment selection or publication decisions.** | |
| ## License | |
| MIT code; CC BY 4.0 training/evaluation dataset (see DATA_LICENSE.md). | |