custom_pipeline.py

from transformers import TextClassificationPipeline, AutoTokenizer

class CustomTextClassificationPipeline(TextClassificationPipeline):
    def __init__(self, model, tokenizer=None, **kwargs):
        # Initialize tokenizer first
        if tokenizer is None:
            tokenizer = AutoTokenizer.from_pretrained(model.config._name_or_path)
        # Make sure we store the tokenizer before calling super().__init__
        self.tokenizer = tokenizer
        super().__init__(model=model, tokenizer=tokenizer, **kwargs)
        
    def _sanitize_parameters(self, **kwargs):
        preprocess_kwargs = {}
        return preprocess_kwargs, {}, {}

    def preprocess(self, inputs):
        return self.tokenizer(inputs, return_tensors='pt', truncation=False)

    def _forward(self, model_inputs):
        input_ids = model_inputs['input_ids']
        attention_mask = (input_ids != 0).long()
        outputs = self.model(input_ids=input_ids, attention_mask=attention_mask)
        return outputs

    def postprocess(self, model_outputs):
        predictions = model_outputs.logits.argmax(dim=-1).squeeze().tolist()
        categories = ["Race/Origin", "Gender/Sex", "Religion", "Ability", "Violence", "Other"]
        return dict(zip(categories, predictions))