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from typing import Dict, List, Any |
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import numpy as np |
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import pickle |
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from sklearn.preprocessing import MultiLabelBinarizer |
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from transformers import AutoTokenizer |
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import torch |
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from eurovoc import EurovocTagger |
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BERT_MODEL_NAME = "nlpaueb/legal-bert-base-uncased" |
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MAX_LEN = 512 |
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TEXT_MAX_LEN = MAX_LEN * 50 |
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tokenizer = AutoTokenizer.from_pretrained(BERT_MODEL_NAME) |
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class EndpointHandler: |
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mlb = MultiLabelBinarizer() |
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def __init__(self, path=""): |
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self.mlb = pickle.load(open(f"{path}/mlb.pickle", "rb")) |
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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self.model = EurovocTagger.from_pretrained(path, |
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bert_model_name=BERT_MODEL_NAME, |
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n_classes=len(self.mlb.classes_), |
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map_location=self.device) |
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self.model.eval() |
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self.model.freeze() |
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
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""" |
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data args: |
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inputs (:obj: `str` | `PIL.Image` | `np.array`) |
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kwargs |
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Return: |
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A :obj:`list` | `dict`: will be serialized and returned |
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""" |
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text = data.pop("inputs", data) |
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topk = data.pop("topk", 5) |
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threshold = data.pop("threshold", 0.16) |
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debug = data.pop("debug", False) |
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prediction = self.get_prediction(text) |
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results = [{"label": label, "score": float(score)} for label, score in |
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zip(self.mlb.classes_, prediction[0].tolist())] |
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results = sorted(results, key=lambda x: x["score"], reverse=True) |
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results = [r for r in results if r["score"] > threshold] |
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results = results[:topk] |
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if debug: |
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return {"results": results, "values": prediction, "input": text} |
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else: |
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return {"results": results} |
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def get_prediction(self, text): |
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chunks = [text[i:i + MAX_LEN] for i in range(0, min(len(text), TEXT_MAX_LEN), MAX_LEN)] |
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predictions = [self._get_prediction(chunk) for chunk in chunks] |
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predictions = np.array(predictions).mean(axis=0) |
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return predictions |
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def _get_prediction(self, text): |
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item = tokenizer.encode_plus( |
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text, |
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add_special_tokens=True, |
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max_length=MAX_LEN, |
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return_token_type_ids=False, |
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padding="max_length", |
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truncation=True, |
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return_attention_mask=True, |
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return_tensors='pt') |
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_, prediction = self.model(item["input_ids"], item["attention_mask"]) |
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prediction = prediction.cpu().detach().numpy() |
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return prediction |
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