ClassiCC-Corpus
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ClassiCC-PT-stem-classifier

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 tags: []
 ---
+# 🎯 ClassiCC-PT Classifiers
+## 📖 Overview
+The ClassiCC-PT classifiers are three BERTimbau-based neural classifiers designed for Portuguese web documents, trained on GPT-4o–annotated data.
+They were created to support content-based filtering in large-scale Portuguese corpora and are part of the ClassiCC-PT dataset pipeline.
+**This repository contains the STEM classifier.**
+The classifiers provide document-level scores (0–5) for:
+    Educational Content (ClassiCC-PT-edu)
+    STEM Content (ClassiCC-PT-STEM)
+    Toxic Content (ClassiCC-PT-toxic)
+## 🏗 Training Setup
+    Base model: BERTimbau Base
+    Head: Linear regression layer
+    Objective: Predict discrete scores (0–5) assigned by GPT-4o
+    Optimizer: AdamW (lr = 3e-4)
+    Scheduler: Cosine decay with 5% warmup
+    Epochs: 20
+    Train Hardware: A100 gpus
+## 📊 Performance
+All classifiers are evaluated both as regressors and in binary classification mode (score ≥ 3 → positive).
+| Classifier        | Task                    | Test Size | Train Size | F1 (Binary) |
+| ----------------- | ----------------------- | --------- | ---------- | ----------- |
+| ClassiCC-PT-edu   | Educational Content     | 10k       | 110k       | **0.77**    |
+| ClassiCC-PT-STEM  | STEM Content            | 12k       | 100k       | **0.76**    |
+| ClassiCC-PT-toxic | Toxic/Offensive Content | 20k       | 180k       | **0.78**    |
+For comparison, the [FineWeb-Edu classifier](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier) (trained only in English) achieved only 0.48 F1 on Portuguese data, highlighting the need for language-specific models.
+## 💡 Intended Use
+These classifiers were built for pretraining corpus filtering but can also be used for:
+    Dataset annotation for educational/STEM/toxic content
+    Research in Portuguese NLP content classification
+    Filtering user-generated content in applications targeting Portuguese speakers
+## Usage
+```
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+model_name = "ClassiCC-Corpus/ClassiCC-PT-edu"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+text = "A fotossíntese é o processo pelo qual as plantas convertem energia luminosa em energia química."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+outputs = model(**inputs)
+score = outputs.logits.squeeze(-1).float().cpu().numpy()
+print(f"Score: {score:.2f}")
+``
+## 📜 Citation
+If you use these classifiers, please cite:
+```
+coming soon
+```