metadata

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 🌐
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:

pip install transformers torch

Loading the Model:

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!

Running Inference:

!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)

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!