NoWayBack/batteryscibert-uncased-abstract-mtc

BatterySciBERT-uncased for Battery Abstract Multi-label Classification

This new model is a fine-tuned version of the BatterySciBERT-uncased model on a few-sample dataset of 1140 abstract of paper.
This model is uncased.

Hyperparameters

batch_size = 4
n_epochs = 16
base_LM_model = "batteryscibert-uncased"
learning_rate = 3e-5

Performance

"Validation Micro F1-score": 94.54,
"Test Micro F1-score": 93.42,

Details on the test set

Predicted Label		Precision	Recall	F1 score
Coating		95.83%	76.67%	85.19%
Computation		86.96%	90.90%	88.89%
Doping		96.30%	100%	98.11%
Experiment		98.02%	93.40%	95.65%
Sodium layered oxide cathode		93.75%	91.84%	92.78%
Aggregate Metric	Micro average	95.51%	91.42%	93.42%
	Macro average	94.17%	90.56%	92.12%

Use in Transformers

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model_name = "NoWayBack/batteryscibert-uncased-abstract-mtc"

# Get predictions
nlp = pipeline('text-classification', model=model_name, tokenizer=model_name, top_k=5)
input_string = "Sodium-ion batteries are among the most promising alternatives to lithium-based " \
               "technologies for grid and other energy storage applications due to their cost benefits " \
               "and sustainable resource supply. For the cathode—the component that largely determines the " \
               "energy density of a sodium-ion battery cell—one major category of materials is P2-type layered oxides."
res = nlp(input_string)

# Load model & tokenizer
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)