File size: 1,312 Bytes
59c3fb3 e66ef1e 59c3fb3 ce64906 59c3fb3 e66ef1e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | 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
"""
# get inputs
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
|