README.md

TP2 — Classical ML vs. embeddings vs. LLMs for text-complexity classification

Binary classification of Spanish financial-education text segments as simple vs. complex, using the FEINA dataset (FEINA_1.xlsx, ~10,600 labeled complex/simplified text pairs). Four approaches are implemented and compared:

1. Perceptron and logistic regression from scratch (LogisticRegression.ipynb) — hand-written forward pass, gradients, and training loop in PyTorch tensors (no nn.Module), with an early-stopping criterion based on gradient-norm convergence, validated on synthetic linearly- and non-linearly-separable data before use on the real task.
2. TF-IDF + logistic regression (compareTFIDF.py) — sweeps 9 tokenizer × lemmatizer combinations (NLTK, regex, spaCy, Stanza tokenizers; WordNet/NLTK, Simplemma, spaCy, Stanza lemmatizers), each evaluated over 3 seeds with an Optuna-calibrated learning rate. Best pipeline: Stanza tokenizer + NLTK stopwords + Stanza lemmatizer.
3. Sentence embeddings + logistic regression (compareEmbeddings.py) — compares distiluse-base-multilingual-cased-v1, multilingual BERT, dccuchile/bert-base-spanish-wwm-cased (BETO), and Buzzeitor/roberta-base-bne.
4. Prompted LLMs (LLMs.py, Phi4_LLM.ipynb, SmolLM3.ipynb) — Microsoft Phi-4-mini-instruct and SmolLM3, run locally (bfloat16, temperature/top_p = 0.1) under zero-shot and few-shot (2/4/7-shot) prompting, parsed from a structured ID:CLASE output.

StadisticTest.ipynb compares all methods statistically: 30 paired 80/20 runs across 6 treatments, analyzed with a Friedman test for global difference, Kendall's W for effect size, and pairwise Wilcoxon signed-rank tests with Holm correction.

Results

Across 30 runs: TF-IDF ≈48.0% precision, embeddings ≈47.9%, SmolLM3 ≈49.8%, Phi-4 ≈49.8% — all close to chance level on this balanced binary task. The Friedman test finds the treatments statistically different overall (χ²=72.23, p≈3.5×10⁻¹⁴), and the LLM-based methods differ significantly from the logistic-regression-based ones (p<0.001), but no method reaches practically useful accuracy — the full analysis and methodology are in documentation/docu.pdf.

Stack

PyTorch, scikit-learn, sentence-transformers, Hugging Face transformers, NLTK, spaCy (es_core_news_lg), Stanza (Spanish models bundled in stanza_models/), Simplemma, Optuna, SciPy, statsmodels.

Running

LogisticRegression.ipynb, compareTFIDF.py, and compareEmbeddings.py run on CPU. Phi4_LLM.ipynb and SmolLM3.ipynb need a CUDA GPU in practice (tested on an RTX 3060 8GB; a full run takes 26–86 minutes per configuration) and a Hugging Face token exported as the HF_TOKEN environment variable before running LLMs.py.