README.md

---
base_model:
- PhaseTechnologies/RoBERTo
tags:
- roberto
language:
- en
pipeline_tag: text-classification
library_name: transformers
---
# RoBERTo-Physics-v1-Finetuned

## Model Overview 🚀

RoBERTo-Physics-v1-Finetuned is a state-of-the-art text classification model fine-tuned on physics-related corpora. Built upon the robust RoBERTa architecture, this model is designed to classify physics-related text into predefined categories with high accuracy and efficiency.

- **Model Name:** RoBERTo-Physics-v1-Finetuned
- **Developer:** [Phase Technologies](https://huggingface.co/PhaseTechnologies) 🌐
- **Model Type:** Text Classification 📚
- **Base Model:** RoBERTa 🏗️
- **Intended Use:** Classifying physics-related texts 🔬

## Model Details 🛠️

- **Pre-trained on:** RoBERTa
- **Fine-tuned on:** Physics Custom Dataset
- **Number of Parameters:** 125M
- **Training Framework:** PyTorch ⚡

## Performance Metrics 📊

| Metric    | Score |
| --------- | ----- |
| Accuracy  | 85%   |
| Precision | 0.82  |
| Recall    | 0.88  |
| F1 Score  | 0.85  |

## How to Use 💡

### Installation:

To use this model, install the required dependencies:

```bash
pip install transformers torch
```

### Loading the Model:

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "PhaseTechnologies/RoBERT-physics-v1-finetuned"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
```
### Demo : 
For demo, visit [PhaseTechnologies/RoBERT-physics-v1-finetuned](https://colab.research.google.com/drive/1BRldXlVpnUufvC7NEi-_bf_ySeQPMbP_?usp=sharing)!
### Running Inference:

```python
!pip install transformers torch
!pip install datasets
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
model_name = "PhaseTechnologies/RoBERTo-physics-v1-finetuned"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def predict(text):
    inputs = tokenizer(text, return_tensors="pt")  # Convert text to model input
    with torch.no_grad():  # No need to calculate gradients
        outputs = model(**inputs)  # Pass input to model
    return outputs.logits  # Return raw predictions

# Example physics-related input
sample_text = "Newton's second law states that force equals mass times acceleration."
logits = predict(sample_text)
print(logits)

```

```python
from transformers import pipeline

# Load the model
classifier = pipeline("text-classification", model="PhaseTechnologies/RoBERTo-physics-v1-finetuned")

# Perform inference
text = "Newton's second law states that force equals mass times acceleration."
result = classifier(text)

print(result)
```

## Intended Use ✅

- Educational and academic research 📚
- Scientific text classification 🔬
- Automated tagging in physics-related content ⚛️

## Limitations ⚠️

- Not suitable for general-purpose text classification ❌
- Performance may degrade with highly technical physics terminology 🧐
- Limited understanding of out-of-domain topics 🌍

## Ethical Considerations 🤝

- The model should be used responsibly for educational and research purposes 📖
- Ensure it is not used to disseminate misinformation 🚫

## Acknowledgments 🙌

This model is the final text classification release from Phase Technologies! 🎉 Thank you to all contributors and researchers who made this possible.

For more details, visit [Phase Technologies on Hugging Face](https://huggingface.co/PhaseTechnologies)!