| from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline | |
| import gradio as gr | |
| import torch | |
| model = AutoModelForQuestionAnswering.from_pretrained("i0xs0/Fine-Tuned-XLM-Question-Answering") | |
| tokenizer = AutoTokenizer.from_pretrained("i0xs0/Fine-Tuned-XLM-Question-Answering") | |
| def generate_answer(question, context): | |
| inputs = tokenizer.encode_plus(question, context, 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 | |
| iface = gr.Interface(fn=generate_answer, | |
| inputs=[gr.Textbox(lines=2, placeholder="Enter Question Here..."), | |
| gr.Textbox(lines=5, placeholder="Enter Context Here...", label="Context")], | |
| outputs=gr.Textbox(lines=5), | |
| title="Question Answering", | |
| description="Type in your question and Context, and the system will provide you with an answer.") | |
| iface.launch() |