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

This model is block sparse: the **linear** layers contains **31.7%** of the original weights.


The model contains **47.0%** of the original weights **overall**.

The training use a modified version of Victor Sanh [Movement Pruning](https://arxiv.org/abs/2005.07683) method.

That means that with the [block-sparse](https://github.com/huggingface/pytorch_block_sparse) runtime it ran **1.12x** 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](https://www.aclweb.org/anthology/N19-1423/) base uncased checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer), and distilled from the equivalent model [csarron/bert-base-uncased-squad-v1](https://huggingface.co/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: 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.

![Pruning details](https://huggingface.co/madlag/bert-base-uncased-squad1.1-block-sparse-0.32-v1/raw/main/model_card/pruning.svg)

## Density plot

<script src="/madlag/bert-base-uncased-squad1.1-block-sparse-0.32-v1/raw/main/model_card/density.js" id="79005f4a-723c-4bf8-bc7f-5ad11676be6c"></script>

## Details

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

### 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**: `355M` (original BERT: `438M`)

| Metric | # Value   | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))|
| ------ | --------- | --------- |
| **EM** | **79.04** | **80.8** |
| **F1** | **86.70** | **88.5** |

## Example Usage

```python
from transformers import pipeline

qa_pipeline = pipeline(
    "question-answering",
    model="madlag/bert-base-uncased-squad1.1-block-sparse-0.32-v1",
    tokenizer="madlag/bert-base-uncased-squad1.1-block-sparse-0.32-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)
```
{"language": "en", "license": "mit", "tags": ["question-answering", "bert", "bert-base"], "datasets": ["squad"], "metrics": ["squad"], "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": "Fr\u00e9d\u00e9ric Fran\u00e7ois Chopin, born Fryderyk Franciszek Chopin (1 March 1810 \u2013 17 October 1849), was a Polish composer and virtuoso pianist of the Romantic era who wrote primarily for solo piano."}]}
transformers pytorch tf bert question-answering bert-base en dataset:squad arxiv:2005.07683 license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #bert #question-answering #bert-base #en #dataset-squad #arxiv-2005.07683 #license-mit #endpoints_compatible #region-us 

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


This model is block sparse: the linear layers contains 31.7% of the original weights.


The model contains 47.0% 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 1.12x 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: 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.


!Pruning details


Density plot
------------



Details
-------


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


### Fine-tuning


* Python: '3.8.5'
* Machine specs:


### Results


Pytorch model file size: '355M' (original BERT: '438M')


Metric: EM, # Value: 79.04, # Original (Table 2): 80.8
Metric: F1, # Value: 86.70, # Original (Table 2): 88.5


Example Usage
-------------



# samples: 90.6K
Dataset: SQuAD1.1, Split: eval, # samples: 11.1k ### Fine-tuning


* Python: '3.8.5'
* Machine specs: ### Results


Pytorch model file size: '355M' (original BERT: '438M')


Metric: EM, # Value: 79.04, # Original (Table 2): 80.8
Metric: F1, # Value: 86.70, # Original (Table 2): 88.5


Example Usage
-------------
TAGS
#transformers #pytorch #tf #bert #question-answering #bert-base #en #dataset-squad #arxiv-2005.07683 #license-mit #endpoints_compatible #region-us 
 # samples: 90.6K
Dataset: SQuAD1.1, Split: eval, # samples: 11.1k ### Fine-tuning


* Python: '3.8.5'
* Machine specs: ### Results


Pytorch model file size: '355M' (original BERT: '438M')


Metric: EM, # Value: 79.04, # Original (Table 2): 80.8
Metric: F1, # Value: 86.70, # Original (Table 2): 88.5


Example Usage
-------------
passage: TAGS
#transformers #pytorch #tf #bert #question-answering #bert-base #en #dataset-squad #arxiv-2005.07683 #license-mit #endpoints_compatible #region-us 
# samples: 90.6K
Dataset: SQuAD1.1, Split: eval, # samples: 11.1k### Fine-tuning


* Python: '3.8.5'
* Machine specs:### Results


Pytorch model file size: '355M' (original BERT: '438M')


Metric: EM, # Value: 79.04, # Original (Table 2): 80.8
Metric: F1, # Value: 86.70, # Original (Table 2): 88.5


Example Usage
-------------