Update app.py
Browse files
app.py
CHANGED
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import gradio as gr
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from transformers import pipeline, AutoModelForQuestionAnswering, AutoTokenizer, AutoModelForTokenClassification
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# Load your custom model and tokenizer
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qa_model_name = "erdometo/xlm-roberta-base-finetuned-TQuad2"
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token_classification_model_name = "
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qa_model = AutoModelForQuestionAnswering.from_pretrained(qa_model_name)
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qa_tokenizer = AutoTokenizer.from_pretrained(qa_model_name)
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token_classification_model = AutoModelForTokenClassification.from_pretrained(token_classification_model_name)
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token_classification_tokenizer = AutoTokenizer.from_pretrained(token_classification_model_name)
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def predict(pipeline_type, question, context):
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if pipeline_type == "question-answering":
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qa_pipeline = pipeline("question-answering", model=qa_model, tokenizer=qa_tokenizer)
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import gradio as gr
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from transformers import pipeline, AutoModelForQuestionAnswering, AutoTokenizer, AutoModelForTokenClassification
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# Load your custom model and tokenizer
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qa_model_name = "erdometo/xlm-roberta-base-finetuned-TQuad2"
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token_classification_model_name = "akdeniz27/convbert-base-turkish-cased-ner"
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qa_model = AutoModelForQuestionAnswering.from_pretrained(qa_model_name)
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qa_tokenizer = AutoTokenizer.from_pretrained(qa_model_name)
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token_classification_model = AutoModelForTokenClassification.from_pretrained(token_classification_model_name)
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token_classification_tokenizer = AutoTokenizer.from_pretrained(token_classification_model_name)
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def predict(pipeline_type, question, context):
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if pipeline_type == "question-answering":
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qa_pipeline = pipeline("question-answering", model=qa_model, tokenizer=qa_tokenizer)
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