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bert-base-uncased-litert

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metadata 
tags:
  - bert
  - transformers
  - litert
  - tflite
  - edge
  - on-device
license: apache-2.0
base_model: google-bert/bert-base-uncased
pipeline_tag: feature-extraction

bert-base-uncased - LiteRT

This is a LiteRT (formerly TensorFlow Lite) conversion of google-bert/bert-base-uncased for efficient on-device inference.

Model Details

Property Value
Original Model google-bert/bert-base-uncased
Format LiteRT (.tflite)
File Size 414.7 MB
Task Feature Extraction / Classification Base
Max Sequence Length 128
Output Dimension 768
Pooling Mode N/A (Full hidden states)

Performance

Benchmarked on AMD CPU (WSL2):

Metric Value
Inference Latency 83.0 ms
Throughput 12.0/sec
Cosine Similarity vs Original 1.0000 ✅

Quick Start 

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

# Load model and tokenizer
interpreter = Interpreter(model_path="google-bert_bert-base-uncased.tflite")
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")

def get_hidden_states(text: str) -> np.ndarray:
    """Get hidden states for input text."""
    encoded = tokenizer(
        text,
        padding="max_length",
        max_length=128,
        truncation=True,
        return_tensors="np"
    )

    interpreter.set_tensor(input_details[0]["index"], encoded["input_ids"].astype(np.int64))
    interpreter.set_tensor(input_details[1]["index"], encoded["attention_mask"].astype(np.int64))
    interpreter.invoke()

    return interpreter.get_tensor(output_details[0]["index"])

# Example
hidden = get_hidden_states("Hello, world!")
cls_embedding = hidden[0, 0, :]  # CLS token for classification
print(f"Hidden shape: {hidden.shape}")  # (1, 128, 768)

Files

  • google-bert_bert-base-uncased.tflite - The LiteRT model file

Conversion Details

  • Conversion Tool: ai-edge-torch
  • Conversion Date: 2026-01-12
  • Source Framework: PyTorch → LiteRT
  • Validation: Cosine similarity 1.0000 vs original

Intended Use

  • Mobile Applications: On-device semantic search, RAG systems
  • Edge Devices: IoT, embedded systems, Raspberry Pi
  • Offline Processing: Privacy-preserving inference
  • Low-latency Applications: Real-time processing

Limitations

  • Fixed sequence length (128 tokens)
  • CPU inference (GPU delegate requires setup)
  • Tokenizer loaded separately from original model
  • Float32 precision

License

This model inherits the license from the original:

  • License: Apache 2.0 (source)

Citation 

@article{devlin2018bert,
    title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
    author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
    journal={arXiv preprint arXiv:1810.04805},
    year={2018}
}

Acknowledgments

Converted by Bombek1