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---

language: en
tags: question answering
license: apache-2.0
datasets: 
- squad
- batterydata/battery-device-data-qa
metrics: squad
---


# BatteryOnlyBERT-cased for QA 
**Language model:** batteryonlybert-cased
**Language:** English  
**Downstream-task:** Extractive QA  
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:**  See [example](https://github.com/ShuHuang/batterybert) 
**Infrastructure**: 8x DGX A100
## Hyperparameters
```

batch_size = 16

n_epochs = 3

base_LM_model = "batteryonlybert-cased"

max_seq_len = 386

learning_rate = 2e-5

doc_stride=128

max_query_length=64

``` 
## Performance
Evaluated on the SQuAD v1.0 dev set.
```

"exact": 79.61,

"f1": 87.30,

```
Evaluated on the battery device dataset.
```

"precision": 64.28,

"recall": 82.72,

```
## Usage
### In Transformers
```python

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline



model_name = "batterydata/batteryonlybert-cased-squad-v1"

# a) Get predictions

nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)

QA_input = {

    'question': 'What is the electrolyte?',

    'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'

}

res = nlp(QA_input)

# b) Load model & tokenizer

model = AutoModelForQuestionAnswering.from_pretrained(model_name)

tokenizer = AutoTokenizer.from_pretrained(model_name)

```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`

Jacqueline Cole: `jmc61 [at] cam.ac.uk`

## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement