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Language model: gelectra-base-germanquad
Language: German
Training data: GermanQuAD train set (~ 12MB)
Eval data: GermanQuAD test set (~ 5MB)
Infrastructure: 1x V100 GPU
Published: Apr 21st, 2021
See https://deepset.ai/germanquad for more details and dataset download in SQuAD format.
batch_size = 24
n_epochs = 2
max_seq_len = 384
learning_rate = 3e-5
lr_schedule = LinearWarmup
embeds_dropout_prob = 0.1
We evaluated the extractive question answering performance on our GermanQuAD test set.
Model types and training data are included in the model name.
For finetuning XLM-Roberta, we use the English SQuAD v2.0 dataset.
The GELECTRA models are warm started on the German translation of SQuAD v1.1 and finetuned on GermanQuAD.
The human baseline was computed for the 3-way test set by taking one answer as prediction and the other two as ground truth.
Timo Möller: timo.moeller@deepset.ai
Julian Risch: julian.risch@deepset.ai
Malte Pietsch: malte.pietsch@deepset.ai
deepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.
Some of our other work:
For more info on Haystack, visit our GitHub repo and Documentation.
We also have a Discord community open to everyone!
Twitter | LinkedIn | Discord | GitHub Discussions | Website
By the way: we're hiring!
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