Dataset Viewer

The dataset viewer is not available for this dataset.

Cannot get the config names for the dataset.

Error code:   ConfigNamesError
Exception:    RuntimeError
Message:      Dataset scripts are no longer supported, but found babylm2025_text_data.py
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1207, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1167, in dataset_module_factory
                  raise RuntimeError(f"Dataset scripts are no longer supported, but found {filename}")
              RuntimeError: Dataset scripts are no longer supported, but found babylm2025_text_data.py

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Overview 📚✨

This repo contains the BabyLM 2025 Text Datasets, released as part of the BabyLM Challenge 2025. It builds on the approach from Diehl Martinez et al. (2023) (CLIMB repo, CLIMB BabyLM2023 Dataset) developed for the 2023 challenge.

⚠️ The dataset format has changed for 2025, so the original preprocessing pipeline had to be adapted

This repo includes currently:

📄 Raw BabyLM 2025 text datasets
🛠 Modified dataset loading script ️
🏷 Datasets tagged with supervised POS tagger from nltk (first of all for compatibility, cannot be used in the actual challenge because of use of outside data) ️

Setup:

Python 3.13.2 virtual environment on macOS
Use pip install -r requirements.txt to install the requirements

✅ TODO's

🧹Update cleaning scripts to fit the new 2025 dataset format
🎯 Unsupervised POS tagging training script missing or unclear where it can be found
- Ideally, train unsupervised POS-tagger on new data using this repo 🎯
🔄 Retrain tokenizer on 2025 data for tagging compatibility in the training / model repo
🚧 Original raw data requires tag features & filename in first line to be directly usable, maybe find a way to tackle that

The rest of this README preserves the original BabyLM 2025 shared task docs for reference (also here). 📑

BabyLM Dataset

This download includes LM Pretraining data for the 2025 shared task, The BabyLM Challenge. The (unzipped) data is not large, only ~700MB.

Contents of this download

babylm_100M: 100M-word training set for the strict track.
babylm_10M: 10M-word training set for the strict-small track.
babylm_dev: Development set for both tracks (10M words)
babylm_test: Test set for both tracks (10M words)

Each directory above contains a single .txt file from each of the 6 domains listed below.

Composition of the data

All datasets are sampled from a mixture of 6 data domains, shown below, along with their respective weights in the distributed dataset.

Source	Weight	Domain	Citation	Website	License
BNC	8%	Dialogue	BNC Consortium (2007)	link	link ¹
CHILDES	29%	Dialogue, Child-Directed	MacWhinney (2000)		link
Project Gutenberg	26%%	Fiction, Nonfiction	Gerlach & Font-Clos (2020)	link	link
OpenSubtitles	20%	Dialogue, Scripted	Lison & Tiedermann (2016)	link	Open source
Simple English Wikipedia	15%	Nonfiction	--	link	link
Switchboard	1%	Dialogue	Godfrey et al. (1992), Stolcke et al., (2000)	link	link

¹ Our distribution of part of the BNC Texts is permitted under the fair dealings provision of copyright law (see term (2g) in the BNC license).

Data preprocessing

Data was minimally preprocessed to conform to a plain text format. We did not tokenize the data. Documents are not necessarily complete are newline separated.

For documentation of the preprocessing pipeline, consult the following repo: https://github.com/babylm/babylm_data_preprocessing

References

BNC Consortium. (2007). The British National Corpus, XML Edition. Oxford Text Archive, http://hdl.handle.net/20.500.12024/2554.

Gerlach, M., & Font-Clos, F. (2020). A standardized Project Gutenberg corpus for statistical analysis of natural language and quantitative linguistics. Entropy, 22(1), 126.

Godfrey, J. J., Holliman, E. C., & McDaniel, J. (1992). SWITCHBOARD: Telephone speech corpus for research and development. In Acoustics, Speech, and Signal Processing, IEEE International Conference on (Vol. 1, pp. 517-520). IEEE Computer Society.

Lison, P. & Tiedemann, J. (2016). OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016).

MacWhinney, B. (2000). The CHILDES Project: Tools for analyzing talk. Third Edition. Mahwah, NJ: Lawrence Erlbaum Associates.

Stolcke, A., Ries, K., Coccaro, N., Shriberg, E., Bates, R., Jurafsky, D., Taylor, P., Martin, R., Van Ess-Dykema, C., & Meteer, M. (2000). Dialogue act modeling for automatic tagging and recognition of conversational speech. Computational linguistics, 26(3), 339-373.

Tiedemann, J. (2012). Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012).

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