minilm-uncased-squad2/README.md

---
language: en
license: cc-by-4.0
datasets:
- squad_v2
model-index:
- name: deepset/minilm-uncased-squad2
  results:
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squad_v2
      type: squad_v2
      config: squad_v2
      split: validation
    metrics:
    - type: exact_match
      value: 76.1921
      name: Exact Match
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmViZTQ3YTBjYTc3ZDQzYmI1Mzk3MTAxM2MzNjdmMTc0MWY4Yzg2MWU3NGQ1MDJhZWI2NzY0YWYxZTY2OTgzMiIsInZlcnNpb24iOjF9.s4XCRs_pvW__LJ57dpXAEHD6NRsQ3XaFrM1xaguS6oUs5fCN77wNNc97scnfoPXT18A8RAn0cLTNivfxZm0oBA
    - type: f1
      value: 79.5483
      name: F1
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmJlYTIyOTg2NjMyMzg4NzNlNGIzMTY2NDVkMjg0ODdiOWRmYjVkZDYyZjBjNWNiNTBhNjcwOWUzMDM4ZWJiZiIsInZlcnNpb24iOjF9.gxpwIBBA3_5xPi-TaZcqWNnGgCiHzxaUNgrS2jucxoVWGxhBtnPdwKVCxLleQoDDZenAXB3Yh71zMP3xTSeHCw
---

# MiniLM-L12-H384-uncased for QA

## Overview
**Language model:** microsoft/MiniLM-L12-H384-uncased  
**Language:** English  
**Downstream-task:** Extractive QA  
**Training data:** SQuAD 2.0  
**Eval data:** SQuAD 2.0  
**Code:**  See [example](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py) in [FARM](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py)  
**Infrastructure**: 1x Tesla v100  

## Hyperparameters

```
seed=42
batch_size = 12
n_epochs = 4
base_LM_model = "microsoft/MiniLM-L12-H384-uncased"
max_seq_len = 384
learning_rate = 4e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.2
doc_stride=128
max_query_length=64
grad_acc_steps=4
```

## Performance
Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
```
"exact": 76.13071675229513,
"f1": 79.49786500219953,
"total": 11873,
"HasAns_exact": 78.35695006747639,
"HasAns_f1": 85.10090269418276,
"HasAns_total": 5928,
"NoAns_exact": 73.91084945332211,
"NoAns_f1": 73.91084945332211,
"NoAns_total": 5945
```

## Usage

### In Transformers
```python
from transformers import AutoModelForQuestionAnswering,  AutoTokenizer, pipeline

model_name = "deepset/minilm-uncased-squad2"

# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
    'question': 'Why is model conversion important?',
    'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
}
res = nlp(QA_input)

# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```

### In FARM

```python
from farm.modeling.adaptive_model import AdaptiveModel
from farm.modeling.tokenization import Tokenizer
from farm.infer import Inferencer

model_name = "deepset/minilm-uncased-squad2"

# a) Get predictions
nlp = Inferencer.load(model_name, task_type="question_answering")
QA_input = [{"questions": ["Why is model conversion important?"],
             "text": "The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks."}]
res = nlp.inference_from_dicts(dicts=QA_input)

# b) Load model & tokenizer
model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering")
tokenizer = Tokenizer.load(model_name)
```

### In haystack
For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in [haystack](https://github.com/deepset-ai/haystack/):
```python
reader = FARMReader(model_name_or_path="deepset/minilm-uncased-squad2")
# or
reader = TransformersReader(model="deepset/minilm-uncased-squad2",tokenizer="deepset/minilm-uncased-squad2")
```


## Authors
**Vaishali Pal:** vaishali.pal@deepset.ai  
**Branden Chan:** branden.chan@deepset.ai  
**Timo Möller:** timo.moeller@deepset.ai  
**Malte Pietsch:** malte.pietsch@deepset.ai   
**Tanay Soni:** tanay.soni@deepset.ai  

## About us
![deepset logo](https://workablehr.s3.amazonaws.com/uploads/account/logo/476306/logo)
We bring NLP to the industry via open source!
Our focus: Industry specific language models & large scale QA systems.

Some of our work: 
- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
- [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)
- [FARM](https://github.com/deepset-ai/FARM)
- [Haystack](https://github.com/deepset-ai/haystack/)

Get in touch:
[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)

By the way: [we're hiring!](http://www.deepset.ai/jobs)