Roberta Base Stress Classification

🚀 roberta-base_stress_classification

This model is a fine - tuned version of [roberta - base](https://huggingface.co/roberta - base) on the glassdoor dataset based on 100000 employees' reviews, aiming to classify stress levels in text.

🚀 Quick Start

This model is a fine - tuned version of [roberta - base](https://huggingface.co/roberta - base) on the glassdoor dataset based on 100000 employees' reviews. It achieves the following results on the evaluation set:

• Loss: 0.1800
• Accuracy: 0.9647
• F1: 0.9647
• Precision: 0.9647
• Recall: 0.9647

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

✨ Features

• Text Classification: Capable of classifying text as either "Stressed" or "Not Stressed".
• High Performance: Achieves high accuracy, F1, precision, and recall scores on the evaluation set.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("dstefa/roberta - base_topic_classification_nyt_news")
model = AutoModelForSequenceClassification.from_pretrained("dstefa/roberta - base_topic_classification_nyt_news")
pipe = pipeline("text - classification", model=model, tokenizer=tokenizer, device=0)

text = "They also caused so much stress because some leaders valued optics over output."
pipe(text)

[{'label': 'Stressed', 'score': 0.9959163069725037}]

📚 Documentation

Training data

Training data was classified as follow:

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e - 05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 5

Training results

Model performance

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2

📄 License

This project is licensed under the MIT license.