tf safetensors albert text-classification en dataset:dair-ai/emotion base_model:albert/albert-large-v2 base_model:finetune:albert/albert-large-v2 license:apache-2.0 model-index region:us
---
base_model:
- albert/albert-large-v2
datasets:
- dair-ai/emotion
language:
- en
license: apache-2.0
metrics:
- accuracy
- f1
- precision
- recall
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.

```python
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](https://huggingface.co/datasets/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.
