pytorch bert question-answering en dataset:squad_v2 transformers license:cc-by-4.0 model-index autotrain_compatible
---
language: en
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:
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By the way: [we're hiring!](http://www.deepset.ai/jobs)