---
license: apache-2.0
language:
- en
metrics:
- name: exact_match
  value: 0.9777
  verified: false
pipeline_tag: text-classification
library_name: sklearn
datasets:
- Lucanix/meta-paper-classify
---
# WITTR or Wait, Is That The References?
A lightweight Naive Bayes classifier  
designed to detect whether a given text should be **filtered out** from an academic paper  
before being used for **language-model pretraining** or **RAG (Retrieval-Augmented Generation)**.

---

## 🧠 Concept

WITTR is trained on text from academic and research-style corpora.  
It distinguishes between two main categories:

- ✅ **0** – meaningful content such as main academic paragraphs, analysis, or discussion  
- ❌ **1** – metadata or non-content text such as author names, URLs, DOIs, references, publication years, or institutional names  

The goal is to provide an **automatic corpus-cleaning step**  
that keeps only the informative text suitable for model training.

---

## ⚙️ Model Details

- **Framework:** scikit-learn  
- **Architecture:** Multinomial Naive Bayes  
- **Vectorization:** TF–IDF  
- **Language:** English academic text  
- **Accuracy:** ≈ 0.9777  
- **Intended Use:** academic text preprocessing, corpus filtering before LLM or RAG pipelines  

---

## 📦 Files

| File | Description |
|------|--------------|
| `wittr_naive.pkl` | Trained Naive Bayes classifier |
| `wittr_naive_vectorizer.pkl` | TF–IDF vectorizer (must be used with the model) |
| `wittr_naive.py` | Simple python script |
| `README.md` | This documentation |

---

## 🚀 Usage Example

```python
from huggingface_hub import hf_hub_download
import joblib

repo = "Lucanix/wittr"

clf = joblib.load(hf_hub_download(repo, "wittr_naive.pkl"))
vectorizer = joblib.load(hf_hub_download(repo, "wittr_naive_vectorizer.pkl"))

text = ["18 Bales G. S. & and Chrzan, D. C. Dynamics of irreversible island growth during submonolayer epitaxy. *Phys. Rev. B* **50**, 6057–6067 (1994)."]
X = vectorizer.transform(text)
print(clf.predict(X))  # → [1]
```