XLM RoBERTa

0-prepare-data.sh

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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

1-train-base.py

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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)

1-train-large.py

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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)

2-eval-base.py

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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')

2-eval-large.py

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