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---
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language: en
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tags: Text Classification
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license: apache-2.0
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datasets:
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- batterydata/paper-abstracts
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metrics: glue
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---
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# BERT-base-uncased for Battery Abstract Classification
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**Language model:** bert-base-uncased
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**Language:** English
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**Downstream-task:** Text Classification
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**Training data:** training\_data.csv
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**Eval data:** val\_data.csv
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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 = 13
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base_LM_model = "bert-base-uncased"
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learning_rate = 2e-5
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```
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## Performance
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```
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"Validation accuracy": 96.79,
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"Test accuracy": 96.29,
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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 AutoModelForSequenceClassification, AutoTokenizer, pipeline
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model_name = "batterydata/bert-base-uncased-abstract"
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# a) Get predictions
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nlp = pipeline('text-classification', model=model_name, tokenizer=model_name)
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input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'}
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res = nlp(input)
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# b) Load model & tokenizer
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model = AutoModelForSequenceClassification.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 |
