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import typing as tp
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
class SciBertPaperClassifier:
def __init__(self, model_path="trained_model"):
self.model = AutoModelForSequenceClassification.from_pretrained(model_path)
self.tokenizer = AutoTokenizer.from_pretrained(model_path)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device)
self.model.eval()
def __call__(self, inputs):
if not isinstance(inputs, tp.Iterable):
inputs = [inputs]
texts = [
f"AUTHORS: {' '.join(paper.authors) if isinstance(paper.authors, list) else paper.authors} "
f"TITLE: {paper.title} ABSTRACT: {paper.abstract}"
for paper in inputs
]
inputs = self.tokenizer(
texts, truncation=True, padding=True, max_length=256, return_tensors="pt"
).to(self.device)
with torch.no_grad():
outputs = self.model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
results = []
for prob in probs:
result = [
{self.model.config.id2label[label_idx]: score.item()}
for label_idx, score in enumerate(prob)
]
results.append(result)
if 1 == len(results):
return results[0]
return results
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