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---
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language: en
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tags: question answering
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license: apache-2.0
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datasets:
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- squad
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- batterydata/battery-device-data-qa
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metrics: squad
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---
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# BatterySciBERT-uncased for QA
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**Language model:** batteryscibert-uncased
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**Language:** English
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**Downstream-task:** Extractive QA
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**Training data:** SQuAD v1
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**Eval data:** SQuAD v1
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**Code:** See [example](https://github.com/ShuHuang/batterybert)
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**Infrastructure**: 8x DGX A100
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## Hyperparameters
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```
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batch_size = 32
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n_epochs = 3
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base_LM_model = "batteryscibert-uncased"
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max_seq_len = 386
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learning_rate = 2e-5
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doc_stride=128
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max_query_length=64
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```
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## Performance
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Evaluated on the SQuAD v1.0 dev set.
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```
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"exact": 79.81,
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"f1": 87.66,
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```
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Evaluated on the battery device dataset.
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```
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"precision": 66.65,
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"recall": 85.29,
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```
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## Usage
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### In Transformers
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```python
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
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model_name = "batterydata/batteryscibert-uncased-squad-v1"
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# a) Get predictions
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nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
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QA_input = {
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'question': 'What is the electrolyte?',
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'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'
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}
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res = nlp(QA_input)
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# b) Load model & tokenizer
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model = AutoModelForQuestionAnswering.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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```
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## Authors
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Shu Huang: `sh2009 [at] cam.ac.uk`
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Jacqueline Cole: `jmc61 [at] cam.ac.uk`
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## Citation
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BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement |
