metadata
language: en
datasets:
- squad_v2
license: mit
thumbnail: >-
https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
tags:
- exbert
Overview
Language model: deepset/roberta-base-squad2-distilled
Language: German
Training data: GermanQuAD train set (~ 12MB)
Eval data: GermanQuAD test set (~ 5MB)
Infrastructure: 4x V100 GPU
Published: Dec 8th, 2021
Details
- haystack's distillation feature was used for training. deepset/roberta-large-squad2 was used as the teacher model.
Hyperparameters
batch_size = 80
n_epochs = 4
max_seq_len = 384
learning_rate = 3e-5
lr_schedule = LinearWarmup
embeds_dropout_prob = 0.1
temperature = 1.5
distillation_loss_weight = 0.75
Performance
"exact": 79.8366040596311
"f1": 83.916407079888
Authors
- Timo M枚ller:
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
Our focus: Industry specific language models & large scale QA systems.
Some of our work:
- German BERT (aka "bert-base-german-cased")
- GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")
- FARM
- Haystack
Get in touch: Twitter | LinkedIn | Slack | GitHub Discussions | Website
By the way: we're hiring!