BatteryBERT-cased for QA
Language model: batterybert-cased
Language: English
Downstream-task: Extractive QA
Training data: SQuAD v1
Eval data: SQuAD v1
Code: See example
Infrastructure: 8x DGX A100
Hyperparameters
batch_size = 16
n_epochs = 4
base_LM_model = "batterybert-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": 81.54,
"f1": 89.16,
Evaluated on the battery device dataset.
"precision": 70.74,
"recall": 84.19,
Usage
In Transformers
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "batterydata/batterybert-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