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	---
	language: en
	thumbnail:
	license: mit
	tags:
	- question-answering
	datasets:
	- squad_v2
	metrics:
	- squad_v2
	widget:
	- text: "Where is the Eiffel Tower located?"
	context: "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eiffel, whose company designed and built the tower."
	- text: "Who is Frederic Chopin?"
	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."
	---

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

	This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the linear layers contains 16.0% of the original weights.



	The model contains 24.0% 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 2.63x 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.

	<div class="graph"><script src="/madlag/bert-large-uncased-wwm-squadv2-x2.63-f82.6-d16-hybrid-v1/raw/main/model_card/density_info.js" id="0e65059e-a61d-4561-947e-b8f47b818bb8"></script></div>

	In terms of accuracy, its F1 is 82.57, compared with 85.85 for bert-large-uncased-whole-word-masking, a F1 drop of 3.28.

	## Fine-Pruning details
	This model was fine-tuned from the HuggingFace [model](https://huggingface.co/bert-large-uncased-whole-word-masking) checkpoint on [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer), and distilled from the model [madlag/bert-large-uncased-whole-word-masking-finetuned-squadv2](https://huggingface.co/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: 190 heads were removed on a total of 384 (49.5%).
	Here is a detailed view on how the remaining heads are distributed in the network after pruning.
	<div class="graph"><script src="/madlag/bert-large-uncased-wwm-squadv2-x2.63-f82.6-d16-hybrid-v1/raw/main/model_card/pruning_info.js" id="f7ae9ec9-d050-46d0-b237-3025165e9504"></script></div>

	## 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:

	```CPU: Intel(R) Core(TM) i7-6700K CPU
	Memory: 64 GiB
	GPUs: 1 GeForce GTX 3090, with 24GiB memory
	GPU driver: 455.23.05, CUDA: 11.1
	```

	### Results

	Pytorch model file size: `1084MB` (original BERT: `1228.0MB`)

	\| Metric \| # Value \| # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))\| Variation \|
	\| ------ \| --------- \| --------- \| --------- \|
	\| EM \| 79.70 \| 82.83 \| -4.13\|
	\| F1 \| 82.57 \| 85.85 \| -3.28\|

	```
	{
	"HasAns_exact": 74.8144399460189,
	"HasAns_f1": 80.555306012496,
	"HasAns_total": 5928,
	"NoAns_exact": 84.57527333894029,
	"NoAns_f1": 84.57527333894029,
	"NoAns_total": 5945,
	"best_exact": 79.70184452118251,
	"best_exact_thresh": 0.0,
	"best_f1": 82.56816761071966,
	"best_f1_thresh": 0.0,
	"exact": 79.70184452118251,
	"f1": 82.56816761071981,
	"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.

	```python
	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-x2.63-f82.6-d16-hybrid-v1",
	tokenizer="madlag/bert-large-uncased-wwm-squadv2-x2.63-f82.6-d16-hybrid-v1"
	)

	print("bert-large-uncased-whole-word-masking parameters: 445.0M")
	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)
	```