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from QBModelWrapperCopy import QBModelWrapper
from QBModelConfig import QBModelConfig
from QBpipeline import QApipeline
from transformers.pipelines import PIPELINE_REGISTRY
from transformers import AutoModelForQuestionAnswering, TFAutoModelForQuestionAnswering
from transformers import pipeline
from transformers import AutoConfig, AutoModel, AutoModelForQuestionAnswering, TFAutoModel
from transformers import AutoTokenizer
config = QBModelConfig()
qb_model = QBModelWrapper(config)
# qa_pipe = QApipeline(model=qb_model)
AutoConfig.register("QA-umd-quizbowl", QBModelConfig)
AutoModel.register(QBModelConfig, QBModelWrapper)
AutoModelForQuestionAnswering.register(QBModelConfig, QBModelWrapper)
# TFAutoModel.register(QBModelConfig, QBModelWrapper)
# TFAutoModelForQuestionAnswering(QBModelConfig, QBModelWrapper)
QBModelConfig.register_for_auto_class()
QBModelWrapper.register_for_auto_class("AutoModel")
QBModelWrapper.register_for_auto_class("AutoModelForQuestionAnswering")
# result = qa_pipe(question="This star in the solar system has 8 planets", context="Context for the question")
# print(result["answer"])
PIPELINE_REGISTRY.register_pipeline(
"qa-pipeline-qb",
pipeline_class=QApipeline,
pt_model=AutoModelForQuestionAnswering,
tf_model=TFAutoModelForQuestionAnswering,
# pt_model=AutoModel,
# tf_model=TFAutoModel
)
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
AutoTokenizer.register(QBModelConfig, tokenizer.slow_tokenizer_class)
qa_pipe = pipeline("qa-pipeline-qb", model=qb_model, tokenizer=tokenizer)
#qa_pipe.push_to_hub("new-attempt-pipeline-2", safe_serialization=False)
qa_pipe.save_pretrained("main", safe_serialization=False)
result = qa_pipe(question="This star in the solar system has 8 planets", context="Context for the question")
print(result)
#if still doesnt work then try making custom pipeline that inherits from QuestionAnsweringPipeline
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