metadata

language: en
datasets:
  - squad_v2
license: cc-by-4.0
model-index:
  - name: deepset/bert-base-uncased-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: 75.6529
            verified: true
          - name: F1
            type: f1
            value: 78.6191
            verified: true
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: adversarial_qa
          type: adversarial_qa
          config: adversarialQA
          split: validation
        metrics:
          - name: Exact Match
            type: exact_match
            value: 23.1333
            verified: true
          - name: F1
            type: f1
            value: 34.5358
            verified: true

bert-base-uncased for QA

Overview

Language model: bert-base-uncased
Language: English
Downstream-task: Extractive QA
Training data: SQuAD 2.0
Eval data: SQuAD 2.0
Infrastructure: 1x Tesla v100

Hyperparameters

batch_size = 32
n_epochs = 3
base_LM_model = "bert-base-uncased"
max_seq_len = 384
learning_rate = 3e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.2
doc_stride=128
max_query_length=64

Performance

"exact": 73.67977764676156
"f1": 77.87647139308865

Authors

Timo Möller: timo.moeller [at] deepset.ai
Julian Risch: julian.risch [at] deepset.ai
Malte Pietsch: malte.pietsch [at] deepset.ai
Michel Bartels: michel.bartels [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 | Slack | GitHub Discussions | Website

By the way: we're hiring!