--- language: en tags: - BabyBERTa license: mit datasets: - CHILDES widget: - text: "Look here. What is that ?" - text: "Do you like your ?" --- ## BabyBERTA ### Overview BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input. It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed. The three provided models are randomly selected from 10 that were trained and reported in the paper. ## Loading the tokenizer BabyBERTa was trained with `add_prefix_space=True`, so it will not work properly with the tokenizer defaults. For instance, to load the tokenizer for BabyBERTa-1, load it as follows: ```python tokenizer = RobertaTokenizerFast.from_pretrained("phueb/BabyBERTa-1", add_prefix_space=True) ``` ### Hyper-Parameters See the paper for details. All provided models were trained for 400K steps with a batch size of 16. Importantly, BabyBERTa never predicts unmasked tokens during training - `unmask_prob` is set to zero. ### Performance BabyBerta was developed for learning grammatical knowledge from child-directed input. Its grammatical knowledge was evaluated using the [Zorro](https://github.com/phueb/Zorro) test suite. The best model achieves an overall accuracy of 80.3, comparable to RoBERTa-base, which achieves an overall accuracy of 82.6 on the latest version of Zorro (as of October, 2021). Both values differ slightly from those reported in the [CoNLL 2021 paper](https://aclanthology.org/2021.conll-1.49/). There are two reasons for this: 1. Performance of RoBERTa-base is slightly larger because the authors previously lower-cased all words in Zorro before evaluation. Lower-casing of proper nouns is detrimental to RoBERTa-base because RoBERTa-base has likely been trained on proper nouns that are primarily title-cased. In contrast, because BabyBERTa is not case-sensitive, its performance is not influenced by this change. 2. The latest version of Zorro no longer contains ambiguous content words such as "Spanish" which can be both a noun and an adjective. this resulted in a small reduction in the performance of BabyBERTa. Overall Accuracy on Zorro: | Model Name | Accuracy (holistic scoring) | Accuracy (MLM-scoring) | |----------------------------------------|------------------------------|------------| | [BabyBERTa-1][link-BabyBERTa-1] | 80.3 | 79.9 | | [BabyBERTa-2][link-BabyBERTa-2] | 78.6 | 78.2 | | [BabyBERTa-3][link-BabyBERTa-3] | 74.5 | 78.1 | ### Additional Information This model was trained by [Philip Huebner](https://philhuebner.com), currently at the [UIUC Language and Learning Lab](http://www.learninglanguagelab.org). More info can be found [here](https://github.com/phueb/BabyBERTa). [link-BabyBERTa-1]: https://huggingface.co/phueb/BabyBERTa-1 [link-BabyBERTa-2]: https://huggingface.co/phueb/BabyBERTa-2 [link-BabyBERTa-3]: https://huggingface.co/phueb/BabyBERTa-3