| 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() |
