
# SqueezeBERT + SQuAD (v1.1)

[squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-uncased) fine-tuned on [SQUAD v1.1](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) for **Q&A** downstream task.

## Details of SqueezeBERT

This model, `squeezebert-uncased`, is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective.
SqueezeBERT was introduced in [this paper](https://arxiv.org/abs/2006.11316). This model is case-insensitive. The model architecture is similar to BERT-base, but with the pointwise fully-connected layers replaced with [grouped convolutions](https://blog.yani.io/filter-group-tutorial/).
The authors found that SqueezeBERT is 4.3x faster than `bert-base-uncased` on a Google Pixel 3 smartphone.
More about the model [here](https://arxiv.org/abs/2004.02984)

## Details of the downstream task (Q&A) - Dataset 📚 🧐 ❓

**S**tanford **Q**uestion **A**nswering **D**ataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
SQuAD v1.1 contains **100,000+** question-answer pairs on **500+** articles.

## Model training 🏋️‍

The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:

```bash
python /content/transformers/examples/question-answering/run_squad.py \
  --model_type bert \
  --model_name_or_path squeezebert/squeezebert-uncased \
  --do_eval \
  --do_train \
  --do_lower_case \
  --train_file /content/dataset/train-v1.1.json \
  --predict_file /content/dataset/dev-v1.1.json \
  --per_gpu_train_batch_size 16 \
  --learning_rate 3e-5 \
  --num_train_epochs 15 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir /content/output_dir \
  --overwrite_output_dir \
  --save_steps 2000
```

## Test set Results 🧾

| Metric | # Value   |
| ------ | --------- |
| **EM** | **76.66** |
| **F1** | **85.83** |

Model Size: **195 MB** 

### Model in action 🚀

Fast usage with **pipelines**:

```python
from transformers import pipeline
QnA_pipeline = pipeline('question-answering', model='mrm8488/squeezebert-finetuned-squadv1')
QnA_pipeline({
    'context': 'A new strain of flu that has the potential to become a pandemic has been identified in China by scientists.',
    'question': 'Who did identified it ?'
    })
    
# Output: {'answer': 'scientists.', 'end': 106, 'score': 0.6988425850868225, 'start': 96}
```

> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)

> Made with <span style="color: #e25555;">&hearts;</span> in Spain

{"language": "en", "datasets": ["squad"]}
transformers pytorch squeezebert question-answering en dataset:squad arxiv:2006.11316 arxiv:2004.02984 endpoints_compatible region:us
TAGS
#transformers #pytorch #squeezebert #question-answering #en #dataset-squad #arxiv-2006.11316 #arxiv-2004.02984 #endpoints_compatible #region-us 

SqueezeBERT + SQuAD (v1.1)
==========================


squeezebert-uncased fine-tuned on SQUAD v1.1 for Q&A downstream task.


Details of SqueezeBERT
----------------------


This model, 'squeezebert-uncased', is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective.
SqueezeBERT was introduced in this paper. This model is case-insensitive. The model architecture is similar to BERT-base, but with the pointwise fully-connected layers replaced with grouped convolutions.
The authors found that SqueezeBERT is 4.3x faster than 'bert-base-uncased' on a Google Pixel 3 smartphone.
More about the model here


Details of the downstream task (Q&A) - Dataset
----------------------------------------------


Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
SQuAD v1.1 contains 100,000+ question-answer pairs on 500+ articles.


Model training ️‍
-----------------


The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:


Test set Results
----------------



Model Size: 195 MB


### Model in action


Fast usage with pipelines:



> 
> Created by Manuel Romero/@mrm8488 | LinkedIn
> 
> 
> 



> 
> Made with ♥ in Spain
> 
> 
> 



### Model in action


Fast usage with pipelines:



> 
> Created by Manuel Romero/@mrm8488 | LinkedIn
> 
> 
> 



> 
> Made with ♥ in Spain
> 
> 
>
TAGS
#transformers #pytorch #squeezebert #question-answering #en #dataset-squad #arxiv-2006.11316 #arxiv-2004.02984 #endpoints_compatible #region-us 
 ### Model in action


Fast usage with pipelines:



> 
> Created by Manuel Romero/@mrm8488 | LinkedIn
> 
> 
> 



> 
> Made with ♥ in Spain
> 
> 
>
passage: TAGS
#transformers #pytorch #squeezebert #question-answering #en #dataset-squad #arxiv-2006.11316 #arxiv-2004.02984 #endpoints_compatible #region-us 
### Model in action


Fast usage with pipelines:



> 
> Created by Manuel Romero/@mrm8488 | LinkedIn
> 
> 
> 



> 
> Made with ♥ in Spain
> 
> 
>