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:
- Distilled roberta-base-squad2 (aka "tinyroberta-squad2")
- German BERT, GermanQuAD and GermanDPR, German embedding model
- deepset Cloud, deepset Studio
Get in touch and join the Haystack community
For more info on Haystack, visit our GitHub repo and Documentation.
We also have a Discord community open to everyone!
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