Principle:Fastai Fastbook Text Data Preparation
| Knowledge Sources | |
|---|---|
| Domains | Natural Language Processing, Text Classification, Data Engineering |
| Last Updated | 2026-02-09 17:00 GMT |
Overview
Text data preparation is the process of acquiring, extracting, and organizing raw text corpora into a structured file layout suitable for downstream NLP pipeline stages such as tokenization, numericalization, and model training.
Description
Before any NLP model can be trained, the raw text data must be downloaded, extracted from its archive format, and organized on disk. For sentiment classification tasks such as IMDb movie review analysis, this involves:
- Downloading a compressed dataset archive from a known URL.
- Extracting the archive into a local directory hierarchy that separates training and test splits, and further separates positive and negative review categories into subfolders.
- Enumerating all individual text files within the extracted structure so that each document can be read and fed into subsequent processing stages.
The IMDb dataset, introduced by Maas et al. (2011), contains 50,000 movie reviews split evenly into 25,000 training and 25,000 test reviews. Each split contains equal numbers of positive and negative reviews. An additional 50,000 unlabeled reviews are available for unsupervised pre-training. The directory layout follows a convention where the parent folder name encodes the label (pos or neg) and the grandparent folder name encodes the split (train or test).
This structured layout is critical for the ULMFiT workflow because:
- The language model fine-tuning stage uses all available text (both labeled and unlabeled) to adapt a pretrained model to the domain.
- The classifier training stage uses only the labeled training split, with labels inferred from the directory structure via a parent_label function.
Usage
Text data preparation is always the first step in any NLP pipeline. Use this technique when:
- Starting a new text classification project with a standard benchmark dataset.
- Working with datasets that follow the "one file per document" convention common in academic NLP benchmarks.
- Needing a reproducible, cached download mechanism that avoids re-downloading data on subsequent runs.
Theoretical Basis
The general workflow for text data preparation can be expressed as the following pseudocode:
FUNCTION prepare_text_data(dataset_url, target_folders):
# Stage 1: Acquire the dataset
local_path = download_and_cache(dataset_url)
# Stage 2: Extract the archive
extracted_path = extract_archive(local_path)
# Stage 3: Enumerate text files
file_list = []
FOR EACH folder IN target_folders:
FOR EACH file IN extracted_path / folder:
IF file.extension == ".txt":
file_list.append(file)
RETURN extracted_path, file_list
Key considerations:
- Caching: The download should be idempotent. If the archive already exists locally, skip the download. This saves bandwidth and time in iterative experimentation.
- Directory structure preservation: The extraction must preserve the original directory hierarchy because downstream components (e.g., GrandparentSplitter, parent_label) rely on folder names to infer data splits and labels.
- File filtering: Not all files in the extracted archive are data files. Configuration files, metadata, and other artifacts should be excluded by filtering on file extension or folder membership.
The IMDb dataset directory structure after extraction:
imdb/
train/
pos/ # 12,500 positive training reviews
0_9.txt
1_7.txt
...
neg/ # 12,500 negative training reviews
0_3.txt
1_1.txt
...
unsup/ # 50,000 unlabeled reviews
0_0.txt
...
test/
pos/ # 12,500 positive test reviews
neg/ # 12,500 negative test reviews
imdb.vocab # vocabulary file
README # dataset documentation