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deepset/
gbert-large-sts

Name: gbert-large-sts
Rating: 2.5 (42 reviews)

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Overview

Language model: gbert-large-sts

Language: German
Training data: German STS benchmark train and dev set
Eval data: German STS benchmark test set
Infrastructure: 1x V100 GPU
Published: August 12th, 2021

Details

We trained a gbert-large model on the task of estimating semantic similarity of German-language text pairs. The dataset is a machine-translated version of the STS benchmark, which is available here.

Hyperparameters

batch_size = 16
n_epochs = 4
warmup_ratio = 0.1
learning_rate = 2e-5
lr_schedule = LinearWarmup

Performance

Stay tuned... and watch out for new papers on arxiv.org ;)

Authors

Julian Risch: julian.risch [at] deepset.ai
Timo Möller: timo.moeller [at] deepset.ai
Julian Gutsch: julian.gutsch [at] deepset.ai
Malte Pietsch: malte.pietsch [at] deepset.ai

About us

deepset is the company behind the production-ready open-source AI framework Haystack.

Some of our other work:

Get in touch and join the Haystack community

For more info on Haystack, visit our GitHub repo and Documentation.

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deepset/gbert-large-sts