--- datasets: - squad_v2 license: cc-by-4.0 model-index: - name: deepset/bert-base-uncased-squad2 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: 75.6529 verified: true - name: F1 type: f1 value: 78.6191 verified: true --- # bert-base-uncased for QA ## Overview **Language model:** bert-base-uncased **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Infrastructure**: 1x Tesla v100 ## Hyperparameters ``` batch_size = 32 n_epochs = 3 base_LM_model = "bert-base-uncased" max_seq_len = 384 learning_rate = 3e-5 lr_schedule = LinearWarmup warmup_proportion = 0.2 doc_stride=128 max_query_length=64 ``` ## Performance ``` "exact": 73.67977764676156 "f1": 77.87647139308865 ``` ## 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 ![deepset logo](https://workablehr.s3.amazonaws.com/uploads/account/logo/476306/logo) We bring NLP to the industry via open source! Our focus: Industry specific language models & large scale QA systems. Some of our work: - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) - [FARM](https://github.com/deepset-ai/FARM) - [Haystack](https://github.com/deepset-ai/haystack/) Get in touch: [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)