README.md · deepset/xlm-roberta-base-squad2-distilled at 8adbba994c9192d032d44fb0d26c473cb484d5e8

metadata

language: multilingual
datasets:
  - squad_v2
license: mit
thumbnail: >-
  https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
tags:
  - exbert
model-index:
  - name: deepset/xlm-roberta-base-squad2-distilled
    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: 75.2485
            verified: true
          - name: F1
            type: f1
            value: 78.3094
            verified: true

Overview

Language model: deepset/roberta-base-squad2-distilled
Language: Multilingual
Training data: SQuAD 2.0 training set
Infrastructure: 1x V100 GPU
Published: Apr 21st, 2021

Details

haystack's distillation feature was used for training. deepset/xlm-roberta-large-squad2 was used as the teacher model.

Hyperparameters

batch_size = 56
n_epochs = 4
max_seq_len = 384
learning_rate = 3e-5
lr_schedule = LinearWarmup
embeds_dropout_prob = 0.1
temperature = 3
distillation_loss_weight = 0.75

Performance

SQuAD v2 dev set:

"exact": 79.8366040596311%
"f1": 83.916407079888%

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!