You can deploy this model and start earning money today!

deepset/
electra-base-squad2

Name: electra-base-squad2
Rating: 2.5 (3671 reviews)

Views

No views yet

electra-base for Extractive QA

Overview

Language model: electra-base
Language: English
Downstream-task: Extractive QA
Training data: SQuAD 2.0
Eval data: SQuAD 2.0
Code: See an example extractive QA pipeline built with Haystack 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.

"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 Haystack

Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack:

python

# After running pip install haystack-ai "transformers[torch,sentencepiece]"

from haystack import Document
from haystack.components.readers import ExtractiveReader

docs = [
    Document(content="Python is a popular programming language"),
    Document(content="python ist eine beliebte Programmiersprache"),
]

reader = ExtractiveReader(model="deepset/roberta-base-squad2")
reader.warm_up()

question = "What is a popular programming language?"
result = reader.run(query=question, documents=docs)
# {'answers': [ExtractedAnswer(query='What is a popular programming language?', score=0.5740374326705933, data='python', document=Document(id=..., content: '...'), context=None, document_offset=ExtractedAnswer.Span(start=0, end=6),...)]}

For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial.

In Transformers

python

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "deepset/roberta-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 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)

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 is the company behind the production-ready open-source AI framework Haystack.

Some of our other work:

Get in touch and join the Haystack community

For more info on Haystack, visit our GitHub repo and Documentation.

We also have a Discord community open to everyone!

By the way: we're hiring!

deepset/electra-base-squad2