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from transformers import Pipeline
from collections.abc import Iterable
import torch
class SciBertPaperClassifierPipeline(Pipeline):
def _sanitize_parameters(self, **kwargs):
return {}, {}, {}
def preprocess(self, inputs):
if not isinstance(inputs, 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)
return inputs
def _forward(self, model_inputs):
with torch.no_grad():
outputs = self.model(**model_inputs)
return outputs
def postprocess(self, model_outputs):
probs = torch.nn.functional.softmax(model_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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