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README.md
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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.
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## Model details:
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All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences.
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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.
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```
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from transformers import AutoTokenizer,
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tokenizer = AutoTokenizer.from_pretrained("catherinearnett/B-GPT_en_pl_simultaneous")
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model = AutoModel.from_pretrained("catherinearnett/B-GPT_en_pl_simultaneous", revision = "128000")
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Text Generation:
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```
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from transformers import pipeline
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pipe = pipeline("text-generation", model="catherinearnett/B-
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pipe("I am a")
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```
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## Citation
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If you use this model, please cite:
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```
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@article{arnett2025acquisition,
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author = {Catherine Arnett and Tyler A. Chang and James A. Michaelov and Benjamin K. Bergen},
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title = {On the Acquisition of Shared Grammatical Representations in Bilingual Language Models},
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journal = {arXiv preprint arXiv:2503.03962},
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year = {2025},
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url = {https://arxiv.org/abs/2503.03962}
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}
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```
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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.
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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.
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## Model details:
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All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences.
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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.
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```
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("catherinearnett/B-GPT_en_nl_sequential")
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model = AutoModelForCausalLM.from_pretrained("catherinearnett/B-GPT_en_nl_sequential", revision = "128000")
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```
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Text Generation:
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```
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from transformers import pipeline
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pipe = pipeline("text-generation", model="catherinearnett/B-GPT_en_nl_sequential")
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print(pipe("I am a", max_length=20)[0]["generated_text"])
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```
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## Citation
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If you use this model, please cite:
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```
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```
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