| 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]]: |
| """ |
| data args: |
| inputs (:obj: `List[str]`) |
| Return: |
| A :obj:`List[List[int]]`: will be serialized and returned |
| """ |
| |
| 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 |
|
|
