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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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from torch.nn.functional import softmax |
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import torch |
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from typing import Any, Dict, List |
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class EndpointHandler: |
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def __init__(self, path=""): |
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self.tokenizer = AutoTokenizer.from_pretrained(path) |
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self.model = AutoModelForSequenceClassification.from_pretrained(path) |
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self.tokenizer.pad_token = self.tokenizer.eos_token |
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self.model.config.pad_token_id = self.tokenizer.pad_token_id |
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
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batch_of_strings = data["inputs"] |
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tokens = self.tokenizer( |
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batch_of_strings, padding=True, truncation=True, return_tensors="pt" |
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) |
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with torch.no_grad(): |
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outputs = self.model(**tokens) |
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probabilities = softmax(outputs.logits, dim=1) |
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return { |
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"predictions": [pred[0] for pred in probabilities.tolist()], |
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} |
