spancnn_pipeline.py

from transformers import pipeline, Pipeline, AutoTokenizer, AutoConfig, AutoModelForSequenceClassification
from transformers.pipelines import PIPELINE_REGISTRY
import torch

class SpanClassificationPipeline(Pipeline):
    def __init__(self, model, tokenizer, device="cpu", **kwargs):
        super().__init__(model=model, tokenizer=tokenizer, device=device, **kwargs)
        self.model.to(self.device)
        self.model.eval()

    def _sanitize_parameters(self, **kwargs):
        return {}, kwargs, {}

    def preprocess(self, inputs):
        return self.tokenizer(inputs, return_tensors="pt").to(self.device)

    def _forward(self, model_inputs):
        with torch.no_grad():
            outputs = self.model(**model_inputs)
        return outputs

    def postprocess(self, model_outputs):
        logits = model_outputs.logits
        return int(torch.argmax(logits, dim=1).item())