metadata

language: en
datasets:
  - squad_v2
license: cc-by-4.0
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:
          - name: Exact Match
            type: exact_match
            value: 77.6074
            verified: true
          - name: F1
            type: f1
            value: 81.7181
            verified: true
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: squadshifts
          type: squadshifts
          config: amazon
          split: test
        metrics:
          - name: Exact Match
            type: exact_match
            value: 64.6108
            verified: true
          - name: F1
            type: f1
            value: 80.0934
            verified: true

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

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 Transformers

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

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:

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

We bring NLP to the industry via open source!
Our focus: Industry specific language models & large scale QA systems.

Some of our work:

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

By the way: we're hiring!