File size: 5,227 Bytes
225a1cb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | # -*- coding: utf-8 -*-
"""code_final2_rob+deb2.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1mVKtHWMKEFD32tvVXNtdQdmAO-EQDWG3
"""
!pip install transformers torch
!pip install accelerate -U
from google.colab import files
import zipfile
import os
uploaded = files.upload()
for fn in uploaded.keys():
print('User uploaded file "{name}" with length {length} bytes'.format(
name=fn, length=len(uploaded[fn])))
if fn.endswith('.zip'):
with zipfile.ZipFile(fn, 'r') as zip_ref:
zip_ref.extractall()
print(f"Files from '{fn}' unzipped in the current directory!")
else:
print(f"'{fn}' is not a zip file.")
os.listdir()
!pip install sentencepiece
import json
import numpy as np
import torch
from transformers import (
DebertaTokenizer, DebertaForSequenceClassification,
RobertaTokenizer, RobertaForSequenceClassification,
DebertaV2Tokenizer, DebertaV2ForSequenceClassification,
BartTokenizer, BartForSequenceClassification,
TrainingArguments, Trainer
)
from torch.utils.data import Dataset
from sklearn.metrics import classification_report
with open('surprise.data', 'r') as data_file:
data = [json.loads(line) for line in data_file]
with open('surprise.solution', 'r') as solution_file:
solutions = [json.loads(line) for line in solution_file]
intent_map = {item['indoml_id']: item['intent'] for item in solutions}
indoml_ids = [item['indoml_id'] for item in data]
intents = [intent_map[indoml_id] for indoml_id in indoml_ids]
utt = [item['utt'] for item in data]
label_map = {intent: i for i, intent in enumerate(set(intents))}
train_labels = [label_map[label] for label in intents]
max_length = 64
class IntentDataset(Dataset):
def __init__(self, encodings, labels):
self.encodings = encodings
self.labels = labels
def __getitem__(self, idx):
item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
item['labels'] = torch.tensor(self.labels[idx])
return item
def __len__(self):
return len(self.labels)
training_args = TrainingArguments(
output_dir='./results',
num_train_epochs=12,
per_device_train_batch_size=16,
per_device_eval_batch_size=64,
warmup_steps=500,
weight_decay=0.01,
logging_dir='./logs',
logging_steps=50,
learning_rate=1.5e-5,
save_strategy="no"
)
# RoBERTa
roberta_tokenizer = RobertaTokenizer.from_pretrained("roberta-large")
roberta_model = RobertaForSequenceClassification.from_pretrained("roberta-large", num_labels=len(label_map))
roberta_train_encodings = roberta_tokenizer(utt, truncation=True, padding=True, max_length=max_length, return_tensors='pt')
roberta_train_dataset = IntentDataset(roberta_train_encodings, train_labels)
roberta_trainer = Trainer(model=roberta_model, args=training_args, train_dataset=roberta_train_dataset)
roberta_trainer.train()
# Common TrainingArguments
training_args = TrainingArguments(
output_dir='./results',
num_train_epochs=10,
per_device_train_batch_size=16,
per_device_eval_batch_size=64,
warmup_steps=500,
weight_decay=0.01,
logging_dir='./logs',
logging_steps=50,
learning_rate=1.5e-5,
save_strategy="no"
)
# DeBERTa v2 XL
debv2_tokenizer = DebertaV2Tokenizer.from_pretrained("microsoft/deberta-v2-xlarge")
debv2_model = DebertaV2ForSequenceClassification.from_pretrained("microsoft/deberta-v2-xlarge", num_labels=len(label_map))
debv2_train_encodings = debv2_tokenizer(utt, truncation=True, padding=True, max_length=max_length, return_tensors='pt')
debv2_train_dataset = IntentDataset(debv2_train_encodings, train_labels)
debv2_trainer = Trainer(model=debv2_model, args=training_args, train_dataset=debv2_train_dataset)
debv2_trainer.train()
# Test predictions
with open('massive_test.data', 'r') as test_file:
test_data = [json.loads(line) for line in test_file]
test_utts = [item['utt'] for item in test_data]
test_ids = [item['indoml_id'] for item in test_data]
roberta_test_encodings = roberta_tokenizer(test_utts, truncation=True, padding=True, max_length=max_length, return_tensors='pt')
debv2_test_encodings = debv2_tokenizer(test_utts, truncation=True, padding=True, max_length=max_length, return_tensors='pt')
roberta_preds = roberta_trainer.predict(IntentDataset(roberta_test_encodings, [0]*len(test_utts))).predictions
debv2_preds = debv2_trainer.predict(IntentDataset(debv2_test_encodings, [0]*len(test_utts))).predictions
ensemble_preds = (roberta_preds + debv2_preds) / 2
predicted_labels = np.argmax(ensemble_preds, axis=1)
predicted_intents = [list(label_map.keys())[list(label_map.values()).index(label)] for label in predicted_labels]
# Create submission file
submission_data = [{"indoml_id": int(test_id), "intent": intent} for test_id, intent in zip(test_ids, predicted_intents)]
submission_filename = 'massive_test_submission.predict'
with open(submission_filename, 'w') as submission_file:
for item in submission_data:
submission_file.write(json.dumps(item) + '\n')
# Zip the submission file
with zipfile.ZipFile("submission.zip", "w") as zipf:
zipf.write(submission_filename) |