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batch_size = 32
n_epochs = 11
base_LM_model = "batteryscibert-cased"
learning_rate = 2e-5"Validation accuracy": 97.06,
"Test accuracy": 97.19,1from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
2model_name = "batterydata/batteryscibert-cased-abstract"
3
4# a) Get predictions
5nlp = pipeline('text-classification', model=model_name, tokenizer=model_name)
6input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'}
7res = nlp(input)
8
9# b) Load model & tokenizer
10model = AutoModelForSequenceClassification.from_pretrained(model_name)
11tokenizer = AutoTokenizer.from_pretrained(model_name)sh2009 [at] cam.ac.ukjmc61 [at] cam.ac.uk