bert-large-uncased-whole-word-masking model fine-tuned on SQuAD v2

This model was created using the nn_pruning python library: the linear layers contains 25.0% of the original weights.

The model contains 32.0% of the original weights overall (the embeddings account for a significant part of the model, and they are not pruned by this method).

With a simple resizing of the linear matrices it ran 2.15x as fast as bert-large-uncased-whole-word-masking on the evaluation. This is possible because the pruning method lead to structured matrices: to visualize them, hover below on the plot to see the non-zero/zero parts of each matrix.

<script src="/madlag/bert-large-uncased-wwm-squadv2-x2.15-f83.2-d25-hybrid-v1/raw/main/model_card/density_info.js" id="d55f6096-07eb-4cc1-b284-90ec6ced516c">

In terms of accuracy, its F1 is 83.22, compared with 85.85 for bert-large-uncased-whole-word-masking, a F1 drop of 2.63.

Fine-Pruning details

This model was fine-tuned from the HuggingFace model checkpoint on SQuAD2.0, and distilled from the model madlag/bert-large-uncased-whole-word-masking-finetuned-squadv2. This model is case-insensitive: it does not make a difference between english and English.

A side-effect of the block pruning is that some of the attention heads are completely removed: 155 heads were removed on a total of 384 (40.4%). Here is a detailed view on how the remaining heads are distributed in the network after pruning.

<script src="/madlag/bert-large-uncased-wwm-squadv2-x2.15-f83.2-d25-hybrid-v1/raw/main/model_card/pruning_info.js" id="a474f11e-7e05-495e-bb21-4af0edfb6661">

Details of the SQuAD1.1 dataset

Dataset Split # samples
SQuAD 2.0 train 130.0K
SQuAD 2.0 eval 11.9k

Fine-tuning

  • Python: 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

Pytorch model file size: 1119MB (original BERT: 1228.0MB)

Metric # Value # Original (Table 2) Variation
EM 80.19 82.83 -3.64
F1 83.22 85.85 -2.63
{
    "HasAns_exact": 76.48448043184885,
    "HasAns_f1": 82.55514100819374,
    "HasAns_total": 5928,
    "NoAns_exact": 83.8856181665265,
    "NoAns_f1": 83.8856181665265,
    "NoAns_total": 5945,
    "best_exact": 80.19034784805862,
    "best_exact_thresh": 0.0,
    "best_f1": 83.22133208932635,
    "best_f1_thresh": 0.0,
    "exact": 80.19034784805862,
    "f1": 83.22133208932645,
    "total": 11873
}

Example Usage

Install nn_pruning: it contains the optimization script, which just 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-large-uncased-wwm-squadv2-x2.15-f83.2-d25-hybrid-v1",
    tokenizer="madlag/bert-large-uncased-wwm-squadv2-x2.15-f83.2-d25-hybrid-v1"
)

print("bert-large-uncased-whole-word-masking parameters: 497.0M")
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)
New

Select AutoNLP in the “Train” menu to fine-tune this model automatically.

Downloads last month
23
Hosted inference API
Question Answering
This model can be loaded on the Inference API on-demand.