Bert Base Uncased Squadv1 X2.32 F86.6 D15 Hybrid V1

🚀 BERT-base uncased model fine-tuned on SQuAD v1

This model is a fine-tuned BERT-base uncased model on the SQuAD v1 dataset, achieving a balance between performance and efficiency through pruning.

🚀 Quick Start

To use this model, you first need to install the nn_pruning library, which contains the optimization script to pack the linear layers into smaller ones by removing empty rows/columns.

pip install nn_pruning

Then you can use the transformers library almost as usual. You just have to call optimize_model when the pipeline has loaded.

from transformers import pipeline
from nn_pruning.inference_model_patcher import optimize_model

qa_pipeline = pipeline(
    "question-answering",
    model="madlag/bert-base-uncased-squadv1-x2.32-f86.6-d15-hybrid-v1",
    tokenizer="madlag/bert-base-uncased-squadv1-x2.32-f86.6-d15-hybrid-v1"
)

print("bert-base-uncased parameters: 165.0M")
print(f"Parameters count (includes only head pruning, not feed forward pruning)={int(qa_pipeline.model.num_parameters() / 1E6)}M")
qa_pipeline.model = optimize_model(qa_pipeline.model, "dense")

print(f"Parameters count after complete optimization={int(qa_pipeline.model.num_parameters() / 1E6)}M")
predictions = qa_pipeline({
    'context': "Frédéric François Chopin, born Fryderyk Franciszek Chopin (1 March 1810 – 17 October 1849), was a Polish composer and virtuoso pianist of the Romantic era who wrote primarily for solo piano.",
    'question': "Who is Frederic Chopin?",
})
print("Predictions", predictions)

✨ Features

• Pruning Optimization: The linear layers of this model contain only 15.0% of the original weights, and overall, it contains 34.0% of the original weights. This pruning method leads to structured matrices, enabling the model to run 2.32x as fast as bert-base-uncased on the evaluation.
• Accuracy Balance: Although there is an F1 drop of 1.86 compared to bert-base-uncased, with an F1 of 86.64, it still maintains a relatively high accuracy level.
• Case-Insensitive: The model does not distinguish between different cases, such as "english" and "English".

📦 Installation

Install the nn_pruning library using the following command:

pip install nn_pruning

💻 Usage Examples

Basic Usage

from transformers import pipeline
from nn_pruning.inference_model_patcher import optimize_model

qa_pipeline = pipeline(
    "question-answering",
    model="madlag/bert-base-uncased-squadv1-x2.32-f86.6-d15-hybrid-v1",
    tokenizer="madlag/bert-base-uncased-squadv1-x2.32-f86.6-d15-hybrid-v1"
)

print("bert-base-uncased parameters: 165.0M")
print(f"Parameters count (includes only head pruning, not feed forward pruning)={int(qa_pipeline.model.num_parameters() / 1E6)}M")
qa_pipeline.model = optimize_model(qa_pipeline.model, "dense")

print(f"Parameters count after complete optimization={int(qa_pipeline.model.num_parameters() / 1E6)}M")
predictions = qa_pipeline({
    'context': "Frédéric François Chopin, born Fryderyk Franciszek Chopin (1 March 1810 – 17 October 1849), was a Polish composer and virtuoso pianist of the Romantic era who wrote primarily for solo piano.",
    'question': "Who is Frederic Chopin?",
})
print("Predictions", predictions)

📚 Documentation

Model Creation

This model was created using the nn_pruning python library. It was fine-tuned from the HuggingFace model checkpoint on SQuAD1.1 and distilled from the model bert-large-uncased-whole-word-masking-finetuned-squad.

Dataset Details

Fine-tuning Details

• Python Version: 3.8.5
• Machine Specs:

Memory: 64 GiB
GPUs: 1 GeForce GTX 3090, with 24GiB memory
GPU driver: 455.23.05, CUDA: 11.1

Results

Fine-Pruning Details

A side-effect of the block pruning is that some of the attention heads are completely removed: 63 heads were removed on a total of 144 (43.8%). You can visualize the non-zero/zero parts of each matrix by hovering on the plot below.

📄 License

This model is released under the MIT license.