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import gradio as gr
from transformers import BertForQuestionAnswering
from transformers import BertTokenizerFast
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
model = BertForQuestionAnswering.from_pretrained("bert-base-uncased")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def get_prediction(context, question):
inputs = tokenizer.encode_plus(question, context, return_tensors='pt').to(device)
outputs = model(**inputs)
answer_start = torch.argmax(outputs[0])
answer_end = torch.argmax(outputs[1]) + 1
answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs['input_ids'][0][answer_start:answer_end]))
return answer
def normalize_text(s):
"""Removing articles and punctuation, and standardizing whitespace are all typical text processing steps."""
import string, re
def remove_articles(text):
regex = re.compile(r"\b(a|an|the)\b", re.UNICODE)
return re.sub(regex, " ", text)
def white_space_fix(text):
return " ".join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def exact_match(prediction, truth):
return bool(normalize_text(prediction) == normalize_text(truth))
def compute_f1(prediction, truth):
pred_tokens = normalize_text(prediction).split()
truth_tokens = normalize_text(truth).split()
# if either the prediction or the truth is no-answer then f1 = 1 if they agree, 0 otherwise
if len(pred_tokens) == 0 or len(truth_tokens) == 0:
return int(pred_tokens == truth_tokens)
common_tokens = set(pred_tokens) & set(truth_tokens)
# if there are no common tokens then f1 = 0
if len(common_tokens) == 0:
return 0
prec = len(common_tokens) / len(pred_tokens)
rec = len(common_tokens) / len(truth_tokens)
return round(2 * (prec * rec) / (prec + rec), 2)
def question_answer(context, question):
prediction = get_prediction(context,question)
return prediction
def greet(texts):
question = texts[:len(texts)]
answer = texts[len(texts):]
for question, answer in texts:
question_answer(context, question)
return texts
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch() |