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

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 = "Hello! How are you?"
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.