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db4a8be9c5
1. add build_doc linter script 2. add sphinx-docs support 3. add development doc and api doc 4. change version to 0.1.0 for the first internal release version Link: https://code.aone.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/8775307
63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
from typing import Any, Dict, Optional, Union
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import numpy as np
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import torch
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from maas_lib.utils.constant import Tasks
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from ..base import Model
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from ..builder import MODELS
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__all__ = ['SequenceClassificationModel']
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@MODELS.register_module(
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Tasks.text_classification, module_name=r'bert-sentiment-analysis')
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class SequenceClassificationModel(Model):
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def __init__(self,
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model_dir: str,
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model_cls: Optional[Any] = None,
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*args,
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**kwargs):
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# Model.__init__(self, model_dir, model_cls, first_sequence, *args, **kwargs)
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# Predictor.__init__(self, *args, **kwargs)
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"""initilize the sequence classification model from the `model_dir` path.
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Args:
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model_dir (str): the model path.
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model_cls (Optional[Any], optional): model loader, if None, use the
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default loader to load model weights, by default None.
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"""
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super().__init__(model_dir, model_cls, *args, **kwargs)
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from easynlp.appzoo import SequenceClassification
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from easynlp.core.predictor import get_model_predictor
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self.model_dir = model_dir
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model_cls = SequenceClassification if not model_cls else model_cls
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self.model = get_model_predictor(
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model_dir=model_dir,
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model_cls=model_cls,
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input_keys=[('input_ids', torch.LongTensor),
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('attention_mask', torch.LongTensor),
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('token_type_ids', torch.LongTensor)],
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output_keys=['predictions', 'probabilities', 'logits'])
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def forward(self, input: Dict[str, Any]) -> Dict[str, np.ndarray]:
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"""return the result by the model
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Args:
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input (Dict[str, Any]): the preprocessed data
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Returns:
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Dict[str, np.ndarray]: results
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Example:
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{
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'predictions': array([1]), # lable 0-negative 1-positive
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'probabilities': array([[0.11491239, 0.8850876 ]], dtype=float32),
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'logits': array([[-0.53860897, 1.5029076 ]], dtype=float32) # true value
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}
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"""
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return self.model.predict(input)
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...
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