custom_pipeline.py

from huggingface_hub.repository import Repository

from transformers import Pipeline, pipeline, AutoModelForSequenceClassification
from transformers.pipelines import PIPELINE_REGISTRY
import numpy as np

from transformers import Pipeline


def softmax(outputs):
    maxes = np.max(outputs, axis=-1, keepdims=True)
    shifted_exp = np.exp(outputs - maxes)
    return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)


class PairClassificationPipeline(Pipeline):
    def _sanitize_parameters(self, **kwargs):
        preprocess_kwargs = {}
        if "second_text" in kwargs:
            preprocess_kwargs["second_text"] = kwargs["second_text"]
        return preprocess_kwargs, {}, {}

    def preprocess(self, text, second_text=None):
        return self.tokenizer(text, text_pair=second_text, return_tensors=self.framework)

    def _forward(self, model_inputs):
        return self.model(**model_inputs)

    def postprocess(self, model_outputs):
        logits = model_outputs.logits[0].numpy()
        probabilities = softmax(logits)

        best_class = np.argmax(probabilities)
        label = self.model.config.id2label[best_class]
        score = probabilities[best_class].item()
        logits = logits.tolist()
        return {"label": label, "score": score, "logits": logits}

PIPELINE_REGISTRY.register_pipeline(
    "new-task",
    pipeline_class=PairClassificationPipeline,
    pt_model=AutoModelForSequenceClassification,
    default={"pt": ("hf-internal-testing/tiny-random-bert", "main")},
    type="text",  # current support type: text, audio, image, multimodal
)

pipe = pipeline("new-task")
print(pipe("This is a test"))

repo = Repository("test-dynamic-pipeline", clone_from="lysandre/test-dynamic-pipeline")
pipe.save_pretrained("test-dynamic-pipeline")
repo.push_to_hub()