README.md

---
language: en
license: mit
library_name: transformers
tags:
- sentiment-analysis
- text-classification
- transformers
- mini-transformer
datasets:
- glue/sst2
model-index:
- name: mini-sentiment-transformer
  results:
  - task:
      type: text-classification
      name: Sentiment Analysis
    dataset:
      name: SST-2
      type: glue
      args: sst2
    metrics:
    - type: accuracy
      value: 0.8154
      name: Validation Accuracy
---

# Mini Sentiment Transformer

This is a tiny transformer model for sentiment analysis, created as a learning project to understand transformer architecture. It's much smaller than BERT or DistilBERT, with only around 4,188,802 parameters.

## Model Details

- Developed by: leorigasaki54
- Type: Text Classification (Sentiment Analysis)
- Language: English
- Training Data: SST-2 (Stanford Sentiment Treebank)
- Size: 4,188,802 parameters (4.19M)
- Architecture:
  - 2 transformer encoder layers
  - 2 attention heads per layer
  - 128 embedding dimensions
  - 256 feed-forward dimensions

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch.nn.functional as F

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")  # We use DistilBERT tokenizer
model = AutoModelForSequenceClassification.from_pretrained("leorigasaki54/mini-sentiment-transformer")

# Prepare input
text = "I really enjoyed this movie!"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=64)

# Make prediction
with torch.no_grad():
    outputs = model(**inputs)
    probabilities = F.softmax(outputs.logits, dim=-1)
    prediction = torch.argmax(probabilities, dim=-1).item()

sentiment = "Positive" if prediction == 1 else "Negative"
confidence = probabilities[0][prediction].item()

print(f"Sentiment: {sentiment} (confidence: {confidence:.4f})")
```

## Limitations

- This is a minimal implementation meant for educational purposes
- Performance may be lower than larger models like BERT or DistilBERT
- The model has been trained only on movie reviews and may not generalize well to other domains
- Limited to English language text only

## Training

The model was trained on the SST-2 dataset for 5 epochs using Adam optimizer with a learning rate of 5e-5.