| from typing import Dict, List, Any | |
| from transformers import pipeline | |
| import torch.nn.functional as F | |
| from torch import Tensor | |
| from transformers import AutoTokenizer, AutoModel | |
| def average_pool(last_hidden_states: Tensor, | |
| attention_mask: Tensor) -> Tensor: | |
| last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) | |
| return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] | |
| class EndpointHandler(): | |
| def __init__(self, path=""): | |
| self.pipeline = pipeline("feature-extraction", model=path) | |
| self.tokenizer = AutoTokenizer.from_pretrained(path) | |
| self.model = AutoModel.from_pretrained(path) | |
| def __call__(self, data: Dict[str, Any]) -> List[List[int]]: | |
| inputs = data.pop("inputs",data) | |
| batch_dict = self.tokenizer(inputs, max_length=512, padding=True, truncation=True, return_tensors='pt') | |
| outputs = self.model(**batch_dict) | |
| embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) | |
| embeddings = F.normalize(embeddings, p=2, dim=1).tolist() | |
| return embeddings |