0-prepare-data.sh

@@ -1,9 +0,0 @@
-#!/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

1-train-base.py

@@ -1,91 +0,0 @@
-#!/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)

1-train-large.py

@@ -1,91 +0,0 @@
-#!/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)

2-eval-base.py

@@ -1,82 +0,0 @@
-#!/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')

2-eval-large.py

@@ -1,82 +0,0 @@
-#!/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')