README.md

---
license: apache-2.0
datasets:
- oscar-corpus/OSCAR-2109
language:
- en
- pl
pipeline_tag: text-generation
library_name: transformers
---

# B-GPT_en_pl_simultaneous

This is a bilingual GPT-2 style model. For the first half of training, this model was trained only on English data. In the second half of training, the model was trained on a 50%-50% mix of English and Polish data. At the end of training, 75% of training data seen by the model is English and 25% is Polish. The tokenizer was trained on the same overall proportions of data as the language model at the final step. 

This model was released alongside the paper [On the Acquisition of Shared Grammatical Representations in Bilingual Language Models](https://arxiv.org/abs/2503.03962), which contains more details about the models. Additionally, the [OSF page](https://osf.io/5cw2e/) provides all code and data related to the project. 

## Model details:

All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences.
For best results, make sure that [CLS] is prepended to your input sequence (see sample usage linked above)!
Details for this model specifically:

* Architecture: gpt2
* Parameters: 124770816
* Maximum sequence length: 512 tokens
* Training tokens: 12B
* Vocabulary size: 50000
* Compute cost: ~9 NVIDIA A6000 GPU hours
* CO2 Emission: 1.17 kg

Training dataset: [OSCAR 2021/09](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)

Checkpoints are taken at training steps: 0, 10000, 20000, 30000, 40000, 50000, 64000, 64010, 64020, 64030, 64040, 64050, 64060, 64070, 64080, 64090, 64100, 64110, 64120, 64130, 64140, 64150, 64160, 64170, 64180, 64190, 64200, 64300, 64400, 64500, 64600, 64700, 64800, 64900, 65000, 66000, 67000, 68000, 69000, 70000, 80000, 90000, 100000, 110000, 120000, 128000.

## Use This Model

Load the model:

Note: if you do not specify a revision, it will load the final checkpoint of the model. See above for the list of checkpoints. The checkpoint step is the name of the revision.

```
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("catherinearnett/B-GPT_en_nl_sequential")
model = AutoModelForCausalLM.from_pretrained("catherinearnett/B-GPT_en_nl_sequential", revision = "128000")
```

Text Generation:

```
from transformers import pipeline

pipe = pipeline("text-generation", model="catherinearnett/B-GPT_en_nl_sequential")
    
print(pipe("I am a", max_length=20)[0]["generated_text"])
```

## Citation

If you use this model, please cite:

```
@article{arnett2025acquisition,
  title={On the Acquisition of Shared Grammatical Representations in Bilingual Language Models},
  author={Arnett, Catherine and Chang, Tyler A and Michaelov, James A and Bergen, Benjamin K},
  journal={arXiv preprint arXiv:2503.03962},
  year={2025}
}
```