BERT-base uncased model fine-tuned on SQuAD v1

This model was created using the nn_pruning python library: the linear layers contains 26.0% of the original weights.

The model contains 42.0% 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 2.44x as fast as the original model 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-base-uncased-squadv1-x2.44-f87.7-d26-hybrid-filled-v1/raw/main/model_card/density_info.js" id="d5d1b3e9-73f5-4cfc-8e33-3745054bc7d0">

In terms of accuracy, its F1 is 87.71, compared with 88.5 for the original model, a F1 drop of 0.79.

Fine-Pruning details

This model was fine-tuned from the HuggingFace model checkpoint on SQuAD1.1, and distilled from the model csarron/bert-base-uncased-squad-v1 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: 80 heads were removed on a total of 144 (55.6%). Here is a detailed view on how the remaining heads are distributed in the network after pruning.

<script src="/madlag/bert-base-uncased-squadv1-x2.44-f87.7-d26-hybrid-filled-v1/raw/main/model_card/pruning_info.js" id="ccef8803-4310-4434-997e-c9dc158cabdb">

Details of the SQuAD1.1 dataset

Dataset Split # samples
SQuAD1.1 train 90.6K
SQuAD1.1 eval 11.1k


  • 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


Pytorch model file size: 355MB (original BERT: 420MB)

Metric # Value # Original (Table 2) Variation
EM 80.03 80.8 -0.77
F1 87.71 88.5 -0.79

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(

print("/home/lagunas/devel/hf/nn_pruning/nn_pruning/analysis/tmp_finetune parameters: 189.0M")
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: fine-tune this model in a few clicks by selecting AutoNLP in the "Train" menu!
Downloads last month
Hosted inference API
Question Answering
This model can be loaded on the Inference API on-demand.