BERT-base uncased model fine-tuned on SQuAD v1
This model is block sparse: the linear layers contains 7.5% of the original weights.
The model contains 28.2% of the original weights overall.
The training use a modified version of Victor Sanh Movement Pruning method.
That means that with the block-sparse runtime it ran 1.92x faster than an dense networks on the evaluation, at the price of some impact on the accuracy (see below).
This model was fine-tuned from the HuggingFace BERT base uncased checkpoint on SQuAD1.1, and distilled from the equivalent model csarron/bert-base-uncased-squad-v1. This model is case-insensitive: it does not make a difference between english and English.
Pruning details
A side-effect of the block pruning is that some of the attention heads are completely removed: 106 heads were removed on a total of 144 (73.6%).
Here is a detailed view on how the remaining heads are distributed in the network after pruning.
Density plot
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Details
| Dataset | Split | # samples |
|---|---|---|
| SQuAD1.1 | train | 90.6K |
| SQuAD1.1 | eval | 11.1k |
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: 335M (original BERT: 438M)
| Metric | # Value | # Original (Table 2) |
|---|---|---|
| EM | 71.88 | 80.8 |
| F1 | 81.36 | 88.5 |
Example Usage
from transformers import pipeline
qa_pipeline = pipeline(
"question-answering",
model="madlag/bert-base-uncased-squad1.1-block-sparse-0.07-v1",
tokenizer="madlag/bert-base-uncased-squad1.1-block-sparse-0.07-v1"
)
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)
