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from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
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):
texts = [
f"AUTHORS: {' '.join(authors) if isinstance(authors, list) else authors} "
f"TITLE: {paper['title']} ABSTRACT: {paper['abstract']}"
for paper in inputs
for authors in [paper.get("authors", "")]
]
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)
scores, labels = torch.max(probs, dim=1)
return [
[{"label": self.model.config.id2label[label.item()], "score": score.item()}]
for label, score in zip(labels, scores)
]
def __getstate__(self):
return self.__dict__
def __setstate__(self, state):
self.__dict__ = state
self.model.to(self.device)
def get_model():
return SciBertPaperClassifier()
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