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| import torch | |
| import streamlit as st | |
| from datasets import Dataset | |
| from torch.utils.data import DataLoader | |
| from transformers import ( | |
| AutoTokenizer, | |
| AutoModelForQuestionAnswering, | |
| TrainingArguments, | |
| Trainer, | |
| default_data_collator, | |
| ) | |
| from lib.utils import preprocess_examples, make_predictions, get_examples | |
| if torch.backends.mps.is_available(): | |
| device = "mps" | |
| elif torch.cuda.is_available(): | |
| device = "cuda" | |
| else: | |
| device = "cpu" | |
| # TO DO: | |
| # - make it pretty | |
| # - add support for multiple questions corresponding to same context | |
| # - add examples | |
| # What else?? | |
| if 'response' not in st.session_state: | |
| st.session_state['response'] = '' | |
| if 'context' not in st.session_state: | |
| st.session_state['context'] = '' | |
| if 'question' not in st.session_state: | |
| st.session_state['question'] = '' | |
| # Build trainer using model and tokenizer from Hugging Face repo | |
| def get_model(): | |
| repo_id = 'etweedy/roberta-base-squad-v2' | |
| model = AutoModelForQuestionAnswering.from_pretrained(repo_id) | |
| tokenizer = AutoTokenizer.from_pretrained(repo_id) | |
| return model, tokenizer | |
| def fill_in_example(i): | |
| st.session_state['response'] = '' | |
| st.session_state['question'] = ex_q[i] | |
| st.session_state['context'] = ex_c[i] | |
| def clear_boxes(): | |
| st.session_state['response'] = '' | |
| st.session_state['question'] = '' | |
| st.session_state['context'] = '' | |
| with st.spinner('Loading the model...'): | |
| model, tokenizer = get_model() | |
| ex_q, ex_c = get_examples() | |
| for i in range(len(ex_q)): | |
| st.sidebar.button( | |
| label = f'Try example {i+1}', | |
| key = f'ex_button_{i+1}', | |
| on_click = fill_in_example, | |
| args=(i,), | |
| ) | |
| st.sidebar.button( | |
| label = 'Clear boxes', | |
| key = 'clear_button', | |
| on_click = clear_boxes, | |
| ) | |
| st.header('RoBERTa Q&A model') | |
| st.markdown(''' | |
| This app demonstrates the answer-retrieval capabilities of a finetuned RoBERTa (Robustly optimized Bidirectional Encoder Representations from Transformers) model. The [RoBERTa base model](https://huggingface.co/roberta-base) was fine-tuned on version 2 of the [SQuAD (Stanford Question Answering Dataset) dataset](https://huggingface.co/datasets/squad_v2), a dataset of context-question-answer triples. The objective of the model is to retrieve the answer to the question from the context paragraph. | |
| Version 2 incorporates the 100,000 samples from Version 1.1, along with 50,000 'unanswerable' questions, i.e. samples in the question cannot be answered using the context given. | |
| Please type or paste a context paragraph and question you'd like to ask about it. The model will attempt to answer the question, or otherwise will report that it cannot. | |
| Alternatively, you can try some of the examples provided on the sidebar to the left. | |
| ''') | |
| input_container = st.container() | |
| st.divider() | |
| response_container = st.container() | |
| # Form for user inputs | |
| with input_container: | |
| with st.form(key='input_form',clear_on_submit=False): | |
| context = st.text_area( | |
| label='Context', | |
| value=st.session_state['context'], | |
| key='context_field', | |
| label_visibility='hidden', | |
| placeholder='Enter your context paragraph here.', | |
| height=300, | |
| ) | |
| st.session_state['context'] = context | |
| question = st.text_input( | |
| label='Question', | |
| value=st.session_state['question'], | |
| key='question_field', | |
| label_visibility='hidden', | |
| placeholder='Enter your question here.', | |
| ) | |
| st.session_state['question'] = question | |
| query_submitted = st.form_submit_button("Submit") | |
| if query_submitted: | |
| with st.spinner('Generating response...'): | |
| data_raw = Dataset.from_dict( | |
| { | |
| 'id':[0], | |
| 'context':[st.session_state['context']], | |
| 'question':[st.session_state['question']], | |
| } | |
| ) | |
| data_proc = data_raw.map( | |
| preprocess_examples, | |
| remove_columns = data_raw.column_names, | |
| batched = True, | |
| fn_kwargs = { | |
| 'tokenizer':tokenizer, | |
| } | |
| ) | |
| predicted_answers = make_predictions(model, tokenizer, | |
| data_proc, data_raw, | |
| n_best = 20) | |
| answer = predicted_answers[0]['prediction_text'] | |
| confidence = predicted_answers[0]['confidence'] | |
| st.session_state['response'] = f""" | |
| Answer: {answer}\n | |
| Confidence: {confidence:.2%} | |
| """ | |
| with response_container: | |
| st.write(st.session_state['response']) | |