| import gradio as gr | |
| import torch | |
| from transformers import AutoTokenizer, RobertaForQuestionAnswering | |
| tokenizer = AutoTokenizer.from_pretrained("tsmatz/roberta_qa_japanese") | |
| model = RobertaForQuestionAnswering.from_pretrained("tsmatz/roberta_qa_japanese") | |
| def answer(text, question): | |
| inputs = tokenizer(question, text, add_special_tokens=True, return_tensors="pt") | |
| input_ids = inputs["input_ids"].tolist()[0] | |
| outputs = model(**inputs) | |
| answer_start_scores = outputs.start_logits | |
| answer_end_scores = outputs.end_logits | |
| answer_start = torch.argmax(answer_start_scores) | |
| answer_end = torch.argmax(answer_end_scores) + 1 | |
| answer = tokenizer.convert_tokens_to_string( | |
| tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]) | |
| ) | |
| return answer | |
| gr.Interface( | |
| answer, | |
| [ | |
| gr.Textbox(label="Text:", placeholder="Text...", lines=5), | |
| gr.Textbox(label="Question:", placeholder="Question...", lines=1) | |
| ], | |
| "text", | |
| ).launch() |