Argument Mining
Collection
BERT models for argument component detection • 4 items • Updated • 1
How to use david-inf/bert-sci-am with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="david-inf/bert-sci-am") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("david-inf/bert-sci-am")
model = AutoModelForSequenceClassification.from_pretrained("david-inf/bert-sci-am", device_map="auto")bert-sci-am is a BERT-family model trained for scientific literature argument mining. At low-level it performs sequence classification. This version is trained on 3-class classification on (david-inf/am-nlp-abstrct)[david-inf/am-nlp-abstrct] forked from pie/abstrct dataset.
from transformers import AutoModelForSequenceClassification, AutoTokenizer
def load_model():
"""Load model from hub"""
checkpoint = "david-inf/bert-sci-am"
model = AutoModelForSequenceClassification.from_pretrained(
checkpoint, num_labels=3)
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
return model, tokenizer
model, tokenizer = load_model()