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| import os | |
| import gradio as gr | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer | |
| import stanza | |
| import re | |
| stanza.download('en', processors='tokenize') | |
| model = AutoModelForSeq2SeqLM.from_pretrained("fangyuan/lfqa_role_classification") | |
| tokenizer = AutoTokenizer.from_pretrained("fangyuan/lfqa_role_classification") | |
| en_nlp = stanza.Pipeline('en', processors='tokenize') | |
| article=''' | |
| ## About | |
| This is a demo for our paper: [How Do We Answer Complex Questions: Discourse Structure of Long-form Answers](https://aclanthology.org/2022.acl-long.249/). | |
| Fangyuan Xu, Junyi Jessy Li, Eunsol Choi. 2022. | |
| ## Model | |
| The model served here is a T5(large)-based role classification model trained on functional roles of ELI5 answers. | |
| ## Resources | |
| Please see more information (paper/code/data/datasheet) at our [website](https://www.cs.utexas.edu/~fxu/lfqa_discourse/index.html). | |
| ## Contact | |
| [Fangyuan Xu](https://www.cs.utexas.edu/~fxu/) via firstname@utexas.edu | |
| ''' | |
| role_mappings = { | |
| 'Answer': 'Answer', | |
| 'Answer (Summary)': 'Summary', | |
| 'Auxiliary Information': 'Auxiliary Information', | |
| 'Answer - Example': 'Example', | |
| 'Miscellaneous': 'Miscellaneous', | |
| 'Answer - Organizational sentence': 'Organizational sentence', | |
| ' ': ' ', | |
| } | |
| def get_ans_sentence_with_stanza(answer_paragraph, pipeline, | |
| is_offset=False): | |
| '''sentence segmentation with stanza''' | |
| answer_paragraph_processed = pipeline(answer_paragraph) | |
| sentences = [] | |
| for sent in answer_paragraph_processed.sentences: | |
| if is_offset: | |
| sentences.append((sent.tokens[0].start_char, sent.tokens[-1].end_char)) | |
| else: | |
| sentence = answer_paragraph[sent.tokens[0].start_char:sent.tokens[-1].end_char + 1] | |
| sentences.append(sentence.strip()) | |
| return sentences | |
| def create_input_to_t5(question, answer): | |
| input_line = [question] | |
| answer_paragraph = get_ans_sentence_with_stanza(answer, en_nlp) | |
| for idx, answer_sent in enumerate(answer_paragraph): | |
| sep_token = '[{}]'.format(idx) | |
| input_line.append(sep_token) | |
| input_line.append(answer_sent) | |
| return ' '.join(input_line) | |
| def process_t5_output(input_txt, output_txt): | |
| pred_roles = [] | |
| answer_sentence = re.split('\[\d+\] ', input_txt) | |
| answer_sentence = answer_sentence[1:] | |
| sentence_idx = re.findall('\[\d+\]', input_txt) | |
| idx_to_sentence = zip(sentence_idx, answer_sentence) | |
| pred_role = re.split('\[\d+\] ', output_txt)[1:] | |
| pred_idx = re.findall('\[\d+\]', output_txt) | |
| idx_to_role = { | |
| idx: role.strip() for (idx, role) in zip(pred_idx, pred_role) | |
| } | |
| for _, (idx, sentence) in enumerate(idx_to_sentence): | |
| pred_role = ' ' if idx not in idx_to_role else idx_to_role[idx] | |
| mapped_pred_role = role_mappings[pred_role] | |
| pred_roles.append('{}: {}'.format(sentence, mapped_pred_role)) | |
| pred_roles.append(output_txt) | |
| return '\n'.join(pred_roles) | |
| def predict(question, answer): | |
| input_txt = create_input_to_t5(question, answer) | |
| input_ids = tokenizer(input_txt, return_tensors='pt').input_ids | |
| outputs = model.generate(input_ids, max_length=512) | |
| output_txt = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] | |
| return process_t5_output(input_txt, output_txt) | |
| gr.Interface( | |
| fn=predict, | |
| inputs=[ | |
| gr.inputs.Textbox(lines=1, label="Question:"), | |
| gr.inputs.Textbox(lines=1, label="Answer:"), | |
| ], | |
| outputs=[ | |
| gr.outputs.Textbox(label="Predicted sentence-level functional roles"), | |
| ], | |
| theme="peach", | |
| title="Discourse structure of long-form answer", | |
| description="Input a question with its long-form answer to see the predicted discourse structure by our role classifier.", | |
| article=article, | |
| examples=[ | |
| #['', ''] | |
| ] | |
| ).launch(enable_queue=True) | |