realm
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78 lines
2.5 KiB
78 lines
2.5 KiB
# Copyright 2021 Zilliz. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import numpy
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from transformers import RealmTokenizer, RealmEmbedder
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from towhee.operator import NNOperator
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from towhee import register
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import warnings
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warnings.filterwarnings('ignore')
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logging.getLogger("transformers").setLevel(logging.ERROR)
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log = logging.getLogger()
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@register(output_schema=['vec'])
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class Realm(NNOperator):
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"""
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NLP embedding operator that uses the pretrained REALM model gathered by huggingface.
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Args:
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model_name (`str`):
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Which model to use for the embeddings.
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"""
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def __init__(self, model_name: str = "google/realm-cc-news-pretrained-embedder") -> None:
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super().__init__()
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self.model_name = model_name
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try:
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self.model = RealmEmbedder.from_pretrained(model_name)
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except Exception as e:
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log.error(f'Fail to load model by name: {self.model_name}')
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raise e
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try:
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self.tokenizer = RealmTokenizer.from_pretrained(model_name)
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except Exception as e:
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log.error(f'Fail to load tokenizer by name: {self.model_name}')
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raise e
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def __call__(self, txt: str) -> numpy.ndarray:
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try:
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inputs = self.tokenizer(txt, return_tensors="pt")
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except Exception as e:
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log.error(f'Invalid input for the tokenizer: {self.model_name}')
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raise e
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try:
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outs = self.model(**inputs)
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except Exception as e:
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log.error(f'Invalid input for the model: {self.model_name}')
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raise e
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try:
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features = outs.projected_score.squeeze(0)
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except Exception as e:
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log.error(f'Fail to extract features by model: {self.model_name}')
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raise e
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vec = features.detach().numpy()
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return vec
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def get_model_list():
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full_list = [
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"google/realm-cc-news-pretrained-embedder"
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]
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full_list.sort()
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return full_list
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