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2 Commits

Author SHA1 Message Date
Karol Kaczmarek
bdac6eb3f3 XLM-RoBERTa - 1 epoch 2020-06-15 10:28:56 +02:00
Karol Kaczmarek
8ce9cb5dac XLM RoBERTa 2020-06-14 18:35:22 +02:00
11 changed files with 857431 additions and 0 deletions

9
0-prepare-data.sh Executable file
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#!/user/bin/env bash
set -e
set -x
for dir_name in 'tran' 'dev-0' 'dev-1' 'test-A'
do
paster data/"${dir_name}"/expected.tsv data/"${dir_name}"/in.tsv > data/"${dir_name}"/data.tsv
done

91
1-train-base.py Executable file
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import gzip
import logging
import lzma
from typing import Optional
import pandas as pd
from tqdm import tqdm
from simpletransformers.classification import ClassificationModel
logger = logging.getLogger(__name__)
def open_file(path, *args):
if path.endswith('gz'):
fopen = gzip.open
elif path.endswith('xz'):
fopen = lzma.open
else:
fopen = open
return fopen(path, *args)
def load_train(path: str, max_lines: Optional[int] = None) -> pd.DataFrame:
"""
Load train/validate data.
Args:
path: file path
max_lines: optional number of lines to read
Returns:
loaded data
"""
data = []
logger.info(f'Loading {path}')
with open_file(path, 'rt') as f:
for i, line in enumerate(tqdm(f)):
line = line.strip()
if '\t' not in line:
logger.error(f'Found empty line at position {i + 1}'
f' - SKIP THIS LINE')
continue
label_name, text = line.split('\t', maxsplit=2)
text = text.strip()
# LABEL should be string number
label_id = int(label_name)
data.append((text, label_id))
if max_lines is not None and i >= max_lines:
break
return pd.DataFrame(data)
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger('transformers')
max_lines = None
train_df = load_train('data/train/train.tsv', max_lines=max_lines)
eval_df = load_train('data/dev-0/data.tsv')
seq = 512
model_name = 'xmlr_base'
args = {
'cache_dir': f'cache_dir-{model_name}/',
'output_dir': f'outputs-{model_name}-{seq}/',
'best_model_dir': f'outputs-{model_name}-{seq}/best_model',
'max_seq_length': seq,
'train_batch_size': 25,
'num_train_epochs': 1,
'evaluate_during_training': True,
'save_steps': 5000,
'evaluate_during_training_steps': 5000,
'use_cached_eval_features': True,
'reprocess_input_data': False,
}
model = ClassificationModel('xlmroberta', 'xlm-roberta-base', args=args,
num_labels=2, use_cuda=True, cuda_device=0)
logger.info(f'START TRAINING | ARGS: {model.args}')
model.train_model(train_df, eval_df=eval_df)

91
1-train-large.py Executable file
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import gzip
import logging
import lzma
from typing import Optional
import pandas as pd
from tqdm import tqdm
from simpletransformers.classification import ClassificationModel
logger = logging.getLogger(__name__)
def open_file(path, *args):
if path.endswith('gz'):
fopen = gzip.open
elif path.endswith('xz'):
fopen = lzma.open
else:
fopen = open
return fopen(path, *args)
def load_train(path: str, max_lines: Optional[int] = None) -> pd.DataFrame:
"""
Load train/validate data.
Args:
path: file path
max_lines: optional number of lines to read
Returns:
loaded data
"""
data = []
logger.info(f'Loading {path}')
with open_file(path, 'rt') as f:
for i, line in enumerate(tqdm(f)):
line = line.strip()
if '\t' not in line:
logger.error(f'Found empty line at position {i + 1}'
f' - SKIP THIS LINE')
continue
label_name, text = line.split('\t', maxsplit=2)
text = text.strip()
# LABEL should be string number
label_id = int(label_name)
data.append((text, label_id))
if max_lines is not None and i >= max_lines:
break
return pd.DataFrame(data)
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger('transformers')
max_lines = None
train_df = load_train('data/train/train.tsv', max_lines=max_lines)
eval_df = load_train('data/dev-0/data.tsv')
seq = 512
model_name = 'xmlr_large'
args = {
'cache_dir': f'cache_dir-{model_name}/',
'output_dir': f'outputs-{model_name}-{seq}/',
'best_model_dir': f'outputs-{model_name}-{seq}/best_model',
'max_seq_length': seq,
'train_batch_size': 10,
'num_train_epochs': 1,
'evaluate_during_training': True,
'save_steps': 5000,
'evaluate_during_training_steps': 5000,
'use_cached_eval_features': True,
'reprocess_input_data': False,
}
model = ClassificationModel('xlmroberta', 'xlm-roberta-large', args=args,
num_labels=2, use_cuda=True, cuda_device=0)
logger.info(f'START TRAINING | ARGS: {model.args}')
model.train_model(train_df, eval_df=eval_df)

82
2-eval-base.py Executable file
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import gzip
import logging
import lzma
import os
from typing import List
import torch
from tqdm import tqdm
from simpletransformers.classification import ClassificationModel
logger = logging.getLogger(__name__)
def open_file(path, *args):
if path.endswith('gz'):
fopen = gzip.open
elif path.endswith('xz'):
fopen = lzma.open
else:
fopen = open
return fopen(path, *args)
def load_test(path: str) -> List[str]:
data = []
logger.debug(f'Loading {path}')
with open_file(path, 'rt') as f:
for line in tqdm(f):
line = line.strip()
data.append(line)
return data
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger('transformers')
transformers_logger.setLevel(logging.WARNING)
for model_name in ['outputs-xmlr_base-512']:
model_dir = os.path.join(model_name, 'best_model')
seq_len = 512
logger.info(f'Processing {model_name} (for sequence length: {seq_len})')
if 'base' in model_name:
model_type = 'base'
elif 'large' in model_name:
model_type = 'large'
else:
raise ValueError(f'Unknown model type in name {model_name}')
args = {
'max_seq_length': seq_len,
'eval_batch_size': 50,
'reprocess_input_data': True,
'sliding_window': False,
}
model = ClassificationModel('xlmroberta', f'{model_dir}',
num_labels=2, args=args,
use_cuda=True, cuda_device=0)
output_name = f'model=xlmr_{model_type}-seq_len={seq_len}'
for test_name in ['dev-0', 'dev-1', 'test-A']:
logger.info(f'Processing {test_name}')
test_data = load_test(f'data/{test_name}/in.tsv')
save_path = f'data/{test_name}/out-{output_name}.tsv'
class_predictions, raw_outputs = model.predict(test_data)
softmax_tensor = torch.nn.functional.softmax(torch.tensor(raw_outputs), dim=1)
logger.info(f'Saving predictions into {save_path}')
with open_file(save_path, 'wt') as w:
for line_id in range(softmax_tensor.size(0)):
line_probs = softmax_tensor[line_id]
# Get second class for (M class)
w.write(f'{line_probs[1].item()}\n')

82
2-eval-large.py Executable file
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import gzip
import logging
import lzma
import os
from typing import List
import torch
from tqdm import tqdm
from simpletransformers.classification import ClassificationModel
logger = logging.getLogger(__name__)
def open_file(path, *args):
if path.endswith('gz'):
fopen = gzip.open
elif path.endswith('xz'):
fopen = lzma.open
else:
fopen = open
return fopen(path, *args)
def load_test(path: str) -> List[str]:
data = []
logger.debug(f'Loading {path}')
with open_file(path, 'rt') as f:
for line in tqdm(f):
line = line.strip()
data.append(line)
return data
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger('transformers')
transformers_logger.setLevel(logging.WARNING)
for model_name in ['outputs-xmlr_large-512']:
model_dir = os.path.join(model_name, 'best_model')
seq_len = 512
logger.info(f'Processing {model_name} (for sequence length: {seq_len})')
if 'base' in model_name:
model_type = 'base'
elif 'large' in model_name:
model_type = 'large'
else:
raise ValueError(f'Unknown model type in name {model_name}')
args = {
'max_seq_length': seq_len,
'eval_batch_size': 35,
'reprocess_input_data': True,
'sliding_window': False,
}
model = ClassificationModel('xlmroberta', f'{model_dir}',
num_labels=2, args=args,
use_cuda=True, cuda_device=0)
output_name = f'model=xlmr_{model_type}-seq_len={seq_len}'
for test_name in ['dev-0', 'dev-1', 'test-A']:
logger.info(f'Processing {test_name}')
test_data = load_test(f'data/{test_name}/in.tsv')
save_path = f'data/{test_name}/out-{output_name}.tsv'
class_predictions, raw_outputs = model.predict(test_data)
softmax_tensor = torch.nn.functional.softmax(torch.tensor(raw_outputs), dim=1)
logger.info(f'Saving predictions into {save_path}')
with open_file(save_path, 'wt') as w:
for line_id in range(softmax_tensor.size(0)):
line_probs = softmax_tensor[line_id]
# Get second class for (M class)
w.write(f'{line_probs[1].item()}\n')

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