---
tags:
- litert
- tflite
- on-device
- classification
license: apache-2.0
base_model: distilbert-base-uncased-finetuned-sst-2-english
---

# distilbert-base-uncased-finetuned-sst-2-english - LiteRT Optimized

This is a [LiteRT](https://ai.google.dev/edge/litert) (formerly TensorFlow Lite) export of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).

It is optimized for mobile and edge inference (Android/iOS/Embedded).

## Model Details
| Attribute | Value |
| :--- | :--- |
| **Task** | Sentiment Analysis |
| **Format** | `.tflite` (Float32) |
| **File Size** | 254.6 MB |
| **Input Length** | 128 tokens |
| **Output Dim** | 2 |

## Usage

```python
import numpy as np
from ai_edge_litert.interpreter import Interpreter
from transformers import AutoTokenizer

model_path = "distilbert-base-uncased-finetuned-sst-2-english.tflite"
interpreter = Interpreter(model_path=model_path)
interpreter.allocate_tensors()

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
labels = ["NEGATIVE", "POSITIVE"]

def predict(text):
    # Tokenize
    inputs = tokenizer(text, max_length=128, padding="max_length", truncation=True, return_tensors="np")

    # Set inputs
    input_details = interpreter.get_input_details()
    interpreter.set_tensor(input_details[0]['index'], inputs['input_ids'].astype(np.int64))
    interpreter.set_tensor(input_details[1]['index'], inputs['attention_mask'].astype(np.int64))

    # Run inference
    interpreter.invoke()

    # Get output (Logits)
    output_details = interpreter.get_output_details()
    logits = interpreter.get_tensor(output_details[0]['index'])[0]

    # Softmax to get probabilities
    probs = np.exp(logits) / np.sum(np.exp(logits))

    # Get top label
    top_idx = np.argmax(probs)
    return labels[top_idx], probs[top_idx]

label, confidence = predict("This is amazing!")
print(f"Result: {label} ({confidence:.2f})")
```

*Converted by [Bombek1](https://huggingface.co/Bombek1) using [litert-torch](https://github.com/google-ai-edge/ai-edge-torch)*