| from typing import Dict, List, Any | |
| from transformers import AutoModel, AutoTokenizer | |
| class EndpointHandler(): | |
| def __init__(self, path=""): | |
| self.tokenizer = AutoTokenizer.from_pretrained("Wellcome/WellcomeBertMesh") | |
| self.model = AutoModel.from_pretrained("Wellcome/WellcomeBertMesh", trust_remote_code=True) | |
| def __call__(self, data: Any) -> List[List[Dict[str, float]]]: | |
| """ | |
| Args: | |
| data (:obj:): | |
| includes the input data and the parameters for the inference. | |
| Return: | |
| A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing : | |
| - "label": A string representing what the label/class is. There can be multiple labels. | |
| - "score": A score between 0 and 1 describing how confident the model is for this label/class. | |
| """ | |
| text = data.pop("inputs", data) | |
| inputs = self.tokenizer(text, padding="max_length") | |
| preds = self.model(input_ids=[inputs["input_ids"]]) | |
| id2label = self.model.config.id2label | |
| prediction = [ | |
| {"label": id2label[label_id], "score": p} | |
| for label_id, p in enumerate(preds[0].tolist()) if p > 0.5 | |
| ] | |
| return prediction | |