SandeepVvigneshwar/sentiment-classification-albert-large-v2

Sentiment classification using Albert-large-v2

Model Description

This model is a fine-tuned version of the ALBERT-Large model designed for emotion sentiment classification, capable of detecting six different emotional categories in text: Anger, Disgust, Fear, Happiness, Sadness, and Surprise. It achieves high performance on sentiment classification tasks, making it suitable for a variety of real-world applications such as emotion detection, content moderation, and sentiment analysis.

Evaluation

Metric	Value
Evaluation Loss	0.08795
Evaluation Accuracy	94.15%
Evaluation Precision	94.90%
Evaluation Recall	94.15%
Evaluation F1-Score	94.25%

How to Get Started

Use the code below to get started with the model.

from transformers import pipeline

emotion_classifier = pipeline("text-classification", model="SandeepVvigneshwar/sentiment-classification-albert-large-v2")

text = "I am so happy to be part of this project!"
emotion = emotion_classifier(text)
print(emotion)

Requirements

• Python 3.x
• Hugging Face transformers library
• PyTorch or TensorFlow

Training Data

dair-ai/emotion

Training Hyperparameters

• learning_rate = 2e-5
• per_device_train_batch_size = 8
• per_device_eval_batch_size = 8
• gradient_accumulation_steps = 2
• num_train_epochs = 8
• weight_decay = 0.01
• fp16 = True
• metric_for_best_model = "f1"
• dataloader_num_workers = 4
• max_grad_norm = 1.0
• lr_scheduler_type = "linear"

Limits

• Domain-specific Text: The model may not perform well on specialized or highly technical texts.
• Languages: The model has been fine-tuned on English-language data and may not generalize well to other languages.
• Input Length: The model performs best with shorter text inputs. For longer, more complex texts, performance may vary.