roberta-base-squad2 for QA on COVID-19


Language model: deepset/roberta-base-squad2
Language: English
Downstream-task: Extractive QA
Training data: SQuAD-style CORD-19 annotations from 23rd April
Code: See example in FARM
Infrastructure: Tesla v100


batch_size = 24
n_epochs = 3
base_LM_model = "deepset/roberta-base-squad2"
max_seq_len = 384
learning_rate = 3e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.1
doc_stride = 128
xval_folds = 5
dev_split = 0
no_ans_boost = -100

license: cc-by-4.0


5-fold cross-validation on the data set led to the following results:

Single EM-Scores: [0.222, 0.123, 0.234, 0.159, 0.158]
Single F1-Scores: [0.476, 0.493, 0.599, 0.461, 0.465]
Single top\_3\_recall Scores: [0.827, 0.776, 0.860, 0.771, 0.777]
XVAL EM: 0.17890995260663506
XVAL f1: 0.49925444207319924
XVAL top\_3\_recall: 0.8021327014218009

This model is the model obtained from the third fold of the cross-validation.


In Transformers

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "deepset/roberta-base-squad2-covid"

# 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)


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

model_name = "deepset/roberta-base-squad2-covid"

# 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, rest_api_schema=True)

# 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:

reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2-covid")
# or 
reader = TransformersReader(model="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2-covid")


Branden Chan: branden.chan [at]
Timo Möller: timo.moeller [at]
Malte Pietsch: malte.pietsch [at]
Tanay Soni: tanay.soni [at]
Bogdan Kostić: bogdan.kostic [at]

About us

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Our focus: Industry specific language models & large scale QA systems.

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