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"""
Code inspired from the Huggingface's transformer library:
File path: transformers/examples/text-classification/run_pl_glue.py
To handle large documents, we use head-truncation. Check the following
paper for a detailed analysis of text classification techniques using
bert-like models: https://arxiv.org/pdf/1905.05583.pdf
"""
import argparse
import glob
import logging
import os
import time
import numpy as np
import torch
from torch.utils.data import DataLoader, TensorDataset
from .base import BaseModule, create_trainer
from .utils import mean_accuracy
logger = logging.getLogger(__name__)
class TextClassification(BaseModule):
mode = 'sequence-classification'
output_mode = 'classification'
example_type = 'text'
def __init__(self, hparams):
super().__init__(hparams)
def _eval_end(self, outputs):
val_loss_mean = torch.stack([x['val_loss'] for x in outputs])\
.mean().detach().cpu().item()
preds = np.concatenate([x['pred'] for x in outputs], axis=0)
preds = np.argmax(preds, axis=1)
out_label_ids = np.concatenate([x['target'] for x in outputs], axis=0)
out_label_list = [[] for _ in range(out_label_ids.shape[0])]
preds_list = [[] for _ in range(out_label_ids.shape[0])]
results = {**{'val_loss': val_loss_mean},
**mean_accuracy(preds, out_label_ids)}
ret = {k: v for k, v in results.items()}
ret['log'] = results
return ret, preds_list, out_label_list
def validation_epoch_end(self, outputs: list) -> dict:
ret, preds, targets = self._eval_end(outputs)
logs = ret['log']
return {'val_loss': logs['val_loss'], 'log': logs, 'progress_bar': logs}
def test_epoch_end(self, outputs):
ret, predictions, targets = self._eval_end(outputs)
# Converting to the dic required by pl
logs = ret['log']
# `val_loss` is the key returned by `self._eval_end()`
# but actually refers to `test_loss`
return {'avg_test_loss': logs['val_loss'],
'log': logs, 'progress_bar': logs}
@staticmethod
def add_model_specific_args(parser, root_dir):
return parser
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