metadata

license: apache-2.0
datasets:
  - dair-ai/emotion
language:
  - en
metrics:
  - accuracy
  - f1
  - precision
  - recall
base_model:
  - albert/albert-large-v2
pipeline_tag: text-classification
model-index:
  - name: SandeepVvigneshwar/sentiment-classification-albert-large-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: emotion
          type: huggingface
          config: default
          split: test
        metrics:
          - type: accuracy
            value: 0.9415
            name: Accuracy
          - type: precision
            value: 0.949
            name: Precision
          - type: recall
            value: 0.9415
            name: Recall
          - type: f1
            value: 0.9425
            name: F1

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 = "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.