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predict.py
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predict.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 24 21:57:13 2020
@author: sarroutim2
"""
import functools
import itertools
import time
from tools import get_dataset
from tools import create_task
from tools import create_mixture
import logging
import argparse
import torch
import transformers
from models import T5Classifier
from models import BERTClassifier
from transformers import BertModel, BertTokenizer
from tools import create_data_loader
import pandas as pd
def predict(args):
inputs = [
"Recent research results suggest that bats or pangolins might be the original hosts for the virus based on comparative studies using its genomic sequences.",
'The coronavirus may have originated in a Chinese laboratory'
]
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
if args.model_type=='t5':
model = T5Classifier(args.model_spec, args.checkpoint, device)
model.predict(
inputs,
sequence_length={"inputs": args.sequence_length_inputs},
batch_size=args.batch_size,
output_file=args.output_file,
)
elif args.model_type=='bert':
checkpoint=args.checkpoint+args.model_spec_b+"/"
model = BERTClassifier(args.model_spec_b, checkpoint, device)
tokenizer = BertTokenizer.from_pretrained(args.model_spec_b)
'''
Get predictions for a simple text
'''
encoded_claim = tokenizer.encode_plus(
inputs[0],
inputs[1],
max_length=args.sequence_length_inputs,
add_special_tokens=True,
return_token_type_ids=False,
pad_to_max_length=True,
return_attention_mask=True,
return_tensors='pt',
)
prediction = model.predict_row(
encoded_claim = encoded_claim,
checkpoint = checkpoint,
device = device
)
labels=['SUPPORTED', 'REFUTED','NOINFO']
print(f'Claim: {inputs[1]}')
print(f'Evidence: {inputs[1]}')
print(f'Label : {labels[prediction[0]]}')
'''
Get predictions for test set
This is similar to the evaluation function, except that we’re storing the text of the claims, evidences
and the predicted probabilities:
'''
df = pd.read_csv(args.data+"dina_test.csv")
test_data_loader = create_data_loader(df, tokenizer, args.sequence_length_inputs, args.batch_size)
y_evidences_texts,y_claims_texts, y_pred, y_test = model.get_predictions(
test_data_loader,
device
)
#print(y_claims_texts)
#print(y_evidences_texts)
#print(y_pred)
#print(y_test)
for claim, evidence, pred_label, gold_label in zip(y_evidences_texts,y_claims_texts,y_pred,y_test):
if labels[pred_label.item()]!=labels[gold_label.item()]:
print('CLAIM: ', claim)
print('EVIDENCE: ', evidence)
print('pred_label: ', labels[pred_label.item()])
print('gold_label: ', labels[gold_label.item()])
else:
raise ValueError("model_type should be either T5 or BERT")
if __name__== '__main__':
parser = argparse.ArgumentParser()
# Session parameters.
# Session parameters.
parser.add_argument('--model-type', type=str, default='bert',
help='model type: BERT or T5')
parser.add_argument('--data', type=str, default='./data/',
help='data for each task')
parser.add_argument('--checkpoint', type=str, default='./checkpoints-healthver/',
help='Path for saving trained models')
parser.add_argument('--model-spec', type=str, default='t5-base',
help='--model-spec: A str to pass into the pretrained_model_name_or_path'
'argument of `transformers.T5ForConditionalGeneration.from_pretrained'
'(e.g. `"t5-base"` or a path to a previously trained model) or an'
'instance of the `transformers.configuration_t5.T5Config` class to use'
'to directly construct the `transformers.T5ForConditionalGeneration object.')
parser.add_argument('--model-spec-b', type=str, default='allenai/scibert_scivocab_uncased',
help='--model-spec: A str to pass into the pretrained_model_name_or_path'
'argument of `transformers.T5ForConditionalGeneration.from_pretrained'
'(e.g. `"t5-base"` or a path to a previously trained model) or an'
'instance of the `transformers.configuration_t5.T5Config` class to use'
'to directly construct the `transformers.T5ForConditionalGeneration object.')
parser.add_argument('--checkpoint-steps', type=str, default='all',
help='Step size for saving trained models')
parser.add_argument('--batch-size', type=int, default=4)
parser.add_argument('--sequence-length-inputs', type=int, default=300)
parser.add_argument('--sequence-length-targets', type=int, default=3)
args = parser.parse_args()
predict(args)