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from transformers import (
AutoConfig,
AutoModelForQuestionAnswering,
AutoTokenizer,
squad_convert_examples_to_features
)
from transformers.data.processors.squad import SquadResult, SquadV2Processor, SquadExample
from transformers.data.metrics.squad_metrics import compute_predictions_logits
import streamlit as st
import gradio as gr
import json
import torch
import time
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
model_checkpoint = "akdeniz27/roberta-base-cuad"
def run_prediction(question_texts, context_text, model_path):
max_seq_length = 512
doc_stride = 256
n_best_size = 1
max_query_length = 64
max_answer_length = 512
do_lower_case = False
null_score_diff_threshold = 0.0
def to_list(tensor):
return tensor.detach().cpu().tolist()
config_class, model_class, tokenizer_class = (
AutoConfig, AutoModelForQuestionAnswering, AutoTokenizer)
config = config_class.from_pretrained(model_path)
tokenizer = tokenizer_class.from_pretrained(
model_path, do_lower_case=True, use_fast=False)
model = model_class.from_pretrained(model_path, config=config)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
processor = SquadV2Processor()
examples = []
for i, question_text in enumerate(question_texts):
example = SquadExample(
qas_id=str(i),
question_text=question_text,
context_text=context_text,
answer_text=None,
start_position_character=None,
title="Predict",
answers=None,
)
examples.append(example)
features, dataset = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=doc_stride,
max_query_length=max_query_length,
is_training=False,
return_dataset="pt",
threads=1,
)
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=10)
all_results = []
for batch in eval_dataloader:
model.eval()
batch = tuple(t.to(device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
}
example_indices = batch[3]
outputs = model(**inputs)
for i, example_index in enumerate(example_indices):
eval_feature = features[example_index.item()]
unique_id = int(eval_feature.unique_id)
output = [to_list(output[i]) for output in outputs.to_tuple()]
start_logits, end_logits = output
result = SquadResult(unique_id, start_logits, end_logits)
all_results.append(result)
final_predictions = compute_predictions_logits(
all_examples=examples,
all_features=features,
all_results=all_results,
n_best_size=n_best_size,
max_answer_length=max_answer_length,
do_lower_case=do_lower_case,
output_prediction_file=None,
output_nbest_file=None,
output_null_log_odds_file=None,
verbose_logging=False,
version_2_with_negative=True,
null_score_diff_threshold=null_score_diff_threshold,
tokenizer=tokenizer
)
return final_predictions
@st.cache(allow_output_mutation=True)
def load_model():
model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint , use_fast=False)
return model, tokenizer
@st.cache(allow_output_mutation=True)
def load_questions():
with open('test.json') as json_file:
data = json.load(json_file)
questions = []
for i, q in enumerate(data['data'][0]['paragraphs'][0]['qas']):
question = data['data'][0]['paragraphs'][0]['qas'][i]['question']
questions.append(question)
return questions
@st.cache(allow_output_mutation=True)
def load_contracts():
with open('test.json') as json_file:
data = json.load(json_file)
contracts = []
for i, q in enumerate(data['data']):
contract = ' '.join(data['data'][i]['paragraphs'][0]['context'].split())
contracts.append(contract)
return contracts
model, tokenizer = load_model()
questions = load_questions()
contracts = load_contracts()
contract = contracts[0]
st.header("📚 Question Answering in Contract Understanding Atticus Dataset (CUAD)")
st.image("contract_review.png")
selected_question = st.selectbox('📑 Choose one of the queries from the CUAD dataset or 📝 write a legal contract and see if the model can answer correctly: ', questions)
question_set = [questions[0], selected_question]
contract_type = st.radio("Select Contract", ("Sample Contract", "New Contract"))
if contract_type == "Sample Contract":
sample_contract_num = st.slider("Select Sample Contract #")
contract = contracts[sample_contract_num]
with st.expander(f"Sample Contract #{sample_contract_num}"):
st.write(contract)
else:
contract = st.text_area("Input New Contract", "", height=256)
Run_Button = st.button("Run", key=None)
if Run_Button == True and not len(contract)==0 and not len(question_set)==0:
predictions = run_prediction(question_set, contract, 'akdeniz27/roberta-base-cuad')
for i, p in enumerate(predictions):
if i != 0: st.write(f"Question: {question_set[int(p)]}\n\nAnswer: {predictions[p]}\n\n")
st.write("🤗")
st.write("Based on Streamlit code of https://huggingface.co/spaces/akdeniz27/contract-understanding-atticus-dataset-demo")
st.write("Model: akdeniz27/roberta-base-cuad")
st.write("Project: https://www.atticusprojectai.org/cuad") |