
# electra-base for QA

## Overview
**Language model:** electra-base  
**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 = 32
n_epochs = 5
base_LM_model = "google/electra-base-discriminator"
max_seq_len = 384
learning_rate = 1e-4
lr_schedule = LinearWarmup
warmup_proportion = 0.1
doc_stride=128
max_query_length=64
```

## Performance
Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
```
"exact": 77.30144024256717,
 "f1": 81.35438272008543,
 "total": 11873,
 "HasAns_exact": 74.34210526315789,
 "HasAns_f1": 82.45961302894314,
 "HasAns_total": 5928,
 "NoAns_exact": 80.25231286795626,
 "NoAns_f1": 80.25231286795626,
 "NoAns_total": 5945
```

## Usage

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

model_name = "deepset/electra-base-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 lets 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/electra-base-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 lets 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 a single paragraph), you can load the model also in [haystack](https://github.com/deepset-ai/haystack/):
```python
reader = FARMReader(model_name_or_path="deepset/electra-base-squad2")
# or
reader = TransformersReader(model="deepset/electra-base-squad2",tokenizer="deepset/electra-base-squad2")
```


## Authors
Vaishali Pal `vaishali.pal [at] deepset.ai`  
Branden Chan: `branden.chan [at] deepset.ai`  
Timo Möller: `timo.moeller [at] deepset.ai`  
Malte Pietsch: `malte.pietsch [at] deepset.ai`  
Tanay Soni: `tanay.soni [at] 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)

{"language": "en", "license": "cc-by-4.0", "datasets": ["squad_v2"], "model-index": [{"name": "deepset/electra-base-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": 77.6074, "name": "Exact Match", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzE5NTRmMmUwYTk1MTI0NjM0ZmQwNDFmM2Y4Mjk4ZWYxOGVmOWI3ZGFiNWM4OTUxZDQ2ZjdmNmU3OTk5ZjRjYyIsInZlcnNpb24iOjF9.0VZRewdiovE4z3K5box5R0oTT7etpmd0BX44FJBLRFfot-uJ915b-bceSv3luJQ7ENPjaYSa7o7jcHlDzn3oAw"}, {"type": "f1", "value": 81.7181, "name": "F1", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2VlMzM0Y2UzYjhhNTJhMTFiYWZmMDNjNjRiZDgwYzc5NWE3N2M4ZGFlYWQ0ZjVkZTE2MDU0YmMzMDc1MTY5MCIsInZlcnNpb24iOjF9.jRV58UxOM7CJJSsmxJuZvlt00jMGA1thp4aqtcFi1C8qViQ1kW7NYz8rg1gNTDZNez2UwPS1NgN_HnnwBHPbCQ"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 80.407, "name": "Exact Match"}, {"type": "f1", "value": 88.942, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "adversarial_qa", "type": "adversarial_qa", "config": "adversarialQA", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 23.533, "name": "Exact Match"}, {"type": "f1", "value": 36.521, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_adversarial", "type": "squad_adversarial", "config": "AddOneSent", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 73.867, "name": "Exact Match"}, {"type": "f1", "value": 81.381, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts amazon", "type": "squadshifts", "config": "amazon", "split": "test"}, "metrics": [{"type": "exact_match", "value": 64.512, "name": "Exact Match"}, {"type": "f1", "value": 80.166, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts new_wiki", "type": "squadshifts", "config": "new_wiki", "split": "test"}, "metrics": [{"type": "exact_match", "value": 76.568, "name": "Exact Match"}, {"type": "f1", "value": 87.706, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts nyt", "type": "squadshifts", "config": "nyt", "split": "test"}, "metrics": [{"type": "exact_match", "value": 77.884, "name": "Exact Match"}, {"type": "f1", "value": 87.858, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts reddit", "type": "squadshifts", "config": "reddit", "split": "test"}, "metrics": [{"type": "exact_match", "value": 64.399, "name": "Exact Match"}, {"type": "f1", "value": 78.096, "name": "F1"}]}]}]}
transformers pytorch safetensors electra question-answering en dataset:squad_v2 license:cc-by-4.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #electra #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us 


# electra-base for QA

## Overview
Language model: electra-base  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Code:  See example in FARM  
Infrastructure: 1x Tesla v100

## Hyperparameters



## Performance
Evaluated on the SQuAD 2.0 dev set with the official eval script.


## Usage

### In Transformers


### In FARM



### In haystack
For doing QA at scale (i.e. many docs instead of a single paragraph), you can load the model also in haystack:



## Authors
Vaishali Pal 'URL [at] URL'  
Branden Chan: 'URL [at] URL'  
Timo Möller: 'timo.moeller [at] URL'  
Malte Pietsch: 'malte.pietsch [at] URL'  
Tanay Soni: 'URL [at] URL'  

## About us
!deepset 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")
- GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")
- FARM
- Haystack

Get in touch:
Twitter | LinkedIn | Discord | GitHub Discussions | Website

By the way: we're hiring!

# electra-base for QA ## Overview
Language model: electra-base  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Code:  See example in FARM  
Infrastructure: 1x Tesla v100 ## Hyperparameters ## Performance
Evaluated on the SQuAD 2.0 dev set with the official eval script. ## Usage ### In Transformers ### In FARM ### In haystack
For doing QA at scale (i.e. many docs instead of a single paragraph), you can load the model also in haystack: ## Authors
Vaishali Pal 'URL [at] URL'  
Branden Chan: 'URL [at] URL'  
Timo Möller: 'timo.moeller [at] URL'  
Malte Pietsch: 'malte.pietsch [at] URL'  
Tanay Soni: 'URL [at] URL' ## About us
!deepset 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")
- GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")
- FARM
- Haystack

Get in touch:
Twitter | LinkedIn | Discord | GitHub Discussions | Website

By the way: we're hiring!
TAGS
#transformers #pytorch #safetensors #electra #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us 
 # electra-base for QA ## Overview
Language model: electra-base  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Code:  See example in FARM  
Infrastructure: 1x Tesla v100 ## Hyperparameters ## Performance
Evaluated on the SQuAD 2.0 dev set with the official eval script. ## Usage ### In Transformers ### In FARM ### In haystack
For doing QA at scale (i.e. many docs instead of a single paragraph), you can load the model also in haystack: ## Authors
Vaishali Pal 'URL [at] URL'  
Branden Chan: 'URL [at] URL'  
Timo Möller: 'timo.moeller [at] URL'  
Malte Pietsch: 'malte.pietsch [at] URL'  
Tanay Soni: 'URL [at] URL' ## About us
!deepset 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")
- GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")
- FARM
- Haystack

Get in touch:
Twitter | LinkedIn | Discord | GitHub Discussions | Website

By the way: we're hiring!
passage: TAGS
#transformers #pytorch #safetensors #electra #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us 
# electra-base for QA## Overview
Language model: electra-base  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Code:  See example in FARM  
Infrastructure: 1x Tesla v100## Hyperparameters## Performance
Evaluated on the SQuAD 2.0 dev set with the official eval script.## Usage### In Transformers### In FARM### In haystack
For doing QA at scale (i.e. many docs instead of a single paragraph), you can load the model also in haystack:## Authors
Vaishali Pal 'URL [at] URL'  
Branden Chan: 'URL [at] URL'  
Timo Möller: 'timo.moeller [at] URL'  
Malte Pietsch: 'malte.pietsch [at] URL'  
Tanay Soni: 'URL [at] URL'## About us
!deepset 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")
- GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")
- FARM
- Haystack

Get in touch:
Twitter | LinkedIn | Discord | GitHub Discussions | Website

By the way: we're hiring!