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---
language:
- en
tags:
- text-classification
- physics
- science
- roberta
- data-cleaning
license: mit
metrics:
- accuracy
- f1
- precision
- recall
base_model: roberta-base
---
# RobertaPhysics: Physics Content Classifier
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) designed to distinguish between **Physics-related content** and **General/Non-Physics text**.
It was developed specifically for **data cleaning pipelines**, aiming to filter and curate high-quality scientific datasets by removing irrelevant noise from raw text collections.
## 📊 Model Performance
The model was trained for 3 epochs and achieved the following results on the validation set (2,191 samples):
| Metric | Value | Interpretation |
| :--- | :--- | :--- |
| **Accuracy** | **94.44%** | Overall correct classification rate. |
| **Precision** | **70.00%** | Reliability when predicting "Physics" class. |
| **Recall** | **62.30%** | Ability to detect Physics content within the dataset. |
| **F1-Score** | **65.93%** | Harmonic mean of precision and recall. |
| **Validation Loss** | **0.1574** | Low validation error indicating stable convergence. |
## 🏷️ Label Mapping
The model uses the following mapping for inference:
* **LABEL_0 (0):** `General` (Non-Physics content, noise, or other topics)
* **LABEL_1 (1):** `Physics` (Scientific or educational content related to physics)
## ⚙️ Training Details
* **Dataset:** Approximately 11,000 processed text samples (8,762 training / 2,191 validation).
* **Architecture:** RoBERTa Base (Sequence Classification).
* **Batch Size:** 16 (Train) / 64 (Eval).
* **Optimizer:** AdamW (weight decay 0.01).
* **Loss Function:** CrossEntropyLoss.
## 🚀 Quick Start
You can use this model directly with the Hugging Face `pipeline`:
```python
from transformers import pipeline
# Load the classifier
classifier = pipeline("text-classification", model="Madras1/RobertaPhysics")
# Example 1: Physics Content
text_physics = "Quantum entanglement describes a phenomenon where linked particles remain connected."
result_physics = classifier(text_physics)
print(result_physics)
# Expected Output: [{'label': 'Physics', 'score': 0.93}]
# Example 2: General Content
text_general = "The quarterly earnings report will be released to investors next Tuesday."
result_general = classifier(text_general)
print(result_general)
# Expected Output: [{'label': 'General', 'score': 0.86}]
```
⚠️ Intended Use
Primary Use: Filtering datasets to retain physics-domain text.
Limitations: The model prioritizes precision over recall (Precision: 70% vs Recall: 62%). This means it is "conservative": it minimizes false positives (junk labeled as physics) but may miss some valid physics texts. This is intentional for high-quality dataset curation. |