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from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline | |
import torch | |
import gradio as grad | |
import ast | |
_pretrainedModelName = "savasy/bert-base-turkish-squad" | |
_tokenizer = AutoTokenizer.from_pretrained(_pretrainedModelName) | |
_model = AutoModelForQuestionAnswering.from_pretrained(_pretrainedModelName) | |
_pipeline = pipeline("question-answering", model = _model, tokenizer = _tokenizer) | |
def answer_question(question, context): | |
text = "{" + "'question': '"+question+"', 'context':'"+context+"'}" | |
di = ast.literal_eval(text) | |
response = _pipeline(di) | |
return response.get("answer") | |
grad.Interface(answer_question, inputs=["text", "text"], outputs=["text"]).launch() | |
''' | |
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline | |
import gradio as grad | |
import ast | |
#_model = "deepset/roberta-base-squad2" | |
_model = "savasy/bert-base-turkish-squad" | |
_pipeline = pipeline("question-answering", model = _model, tokenizer = _model) | |
def answer_question(question, context): | |
text = "{" + "'question': '"+question+"', 'context':'"+context+"'}" | |
di = ast.literal_eval(text) | |
response = _pipeline(di) | |
return response | |
grad.Interface(answer_question, inputs=["text", "text"], outputs="text").launch() | |
''' | |