| from QBModelConfig import QBModelConfig | |
| from QBModelWrapper import QBModelWrapper | |
| from transformers import AutoConfig, AutoModel, AutoModelForQuestionAnswering | |
| import torch | |
| import numpy as np | |
| from transformers import QuestionAnsweringPipeline | |
| from transformers import PretrainedConfig | |
| from transformers.pipelines import PIPELINE_REGISTRY | |
| from transformers import AutoModelForQuestionAnswering, TFAutoModelForQuestionAnswering | |
| from transformers import pipeline | |
| # class DemoQAPipeline(QuestionAnsweringPipeline): | |
| # def postprocess(self, model_outputs): | |
| # answers = super().postprocess(model_outputs) | |
| # return {'guess': answers['answer'], 'confidence': answers['score']} | |
| AutoConfig.register("QA-umd-quizbowl", QBModelConfig) | |
| AutoModel.register(QBModelConfig, QBModelWrapper) | |
| AutoModelForQuestionAnswering.register(QBModelConfig, QBModelWrapper) | |
| QBModelConfig.register_for_auto_class() | |
| QBModelWrapper.register_for_auto_class("AutoModel") | |
| QBModelWrapper.register_for_auto_class("AutoModelForQuestionAnswering") | |
| # PIPELINE_REGISTRY.register_pipeline( | |
| # "demo-qa", | |
| # pipeline_class=DemoQAPipeline, | |
| # pt_model=AutoModelForQuestionAnswering, | |
| # tf_model=TFAutoModelForQuestionAnswering, | |
| # ) | |
| qbmodel_config = QBModelConfig() | |
| qbmodel = QBModelWrapper(qbmodel_config) | |
| # # qbmodel_config.save_pretrained("hf-model-config") | |
| #qbmodel.save_pretrained(save_directory='hf-model-save', safe_serialization= False, push_to_hub=True) | |
| qbmodel.push_to_hub("quiz-bowl-model-qa-new-attempt-v2") | |
| # model = AutoModelForQuestionAnswering.from_pretrained("nes470/hf-model-save", trust_remote_code = True) | |
| # qa_pipe = pipeline("question-answering", model=model) | |