---
library_name: transformers
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
- emotion-classification
- text2text-generation
- flan-t5
- linkspreed
- web4-ai
model-index:
- name: LS-W4-T5-SM-Emotions
  results: []
---

# LS-W4-T5-SM-Emotions

## Model Description
This is a fine-tuned version of the **[google/flan-t5-small](https://huggingface.co/google/flan-t5-small)** model, trained for the specific task of **emotion classification from text**.  
The model takes a text input and generates a single word indicating the primary emotion.  
It was fine-tuned on the [dair-ai/emotion](https://huggingface.co/dair-ai/emotion) dataset.

- **Developer:** Linkspreed x Web4 AI  
- **Base Model:** google/flan-t5-small  
- **Model Type:** Encoder-Decoder (Text-to-Text)  

---

## Intended Use
This model is intended for **research and educational purposes**.  
It can be used to classify the sentiment of short texts, such as **social media posts, comments, or short sentences**, into one of six categories:

- joy  
- sadness  
- anger  
- love  
- fear  
- surprise  

---

## Training Data
The model was fine-tuned on the **dair-ai/emotion** dataset, which contains **20,000 English social media messages**.

- **Training set:** 16,000 examples  
- **Validation set:** 2,000 examples  
- **Test set:** 2,000 examples  

⚠️ **Note:**  
The training data is highly **imbalanced**, with *joy* and *anger* being the most frequent emotions.  
This may lead to a bias where the model **over-predicts these two classes** and performs poorly on the less frequent ones.

---

## Training Details

### Training Hyperparameters
The following hyperparameters were used during training:
- **learning_rate:** 5e-05  
- **train_batch_size:** 8  
- **eval_batch_size:** 8  
- **seed:** 42  
- **optimizer:** AdamW (torch fused) with betas=(0.9, 0.999), epsilon=1e-08  
- **lr_scheduler_type:** linear  
- **num_epochs:** 3  

### Training Setup
- **Framework:** PyTorch & Hugging Face Transformers  
- **Hardware:** NVIDIA T4 GPU  

---

## How to Use

You can use this model directly with the Hugging Face `pipeline` for quick inference:

```python
from transformers import pipeline

model_id = "Web4/LS-W4-T5-SM-Emotions"
analyst = pipeline("text2text-generation", model=model_id)

text_to_analyze = "sentiment: I am so happy about my new job!"
result = analyst(text_to_analyze)
print(result)

# Example output:
# [{'generated_text': 'joy'}]