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README.md
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---
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license: mit
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datasets:
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- omarmomen/babylm_10M
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language:
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- en
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metrics:
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- perplexity
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library_name: transformers
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---
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# Model Card for omarmomen/structroberta_s2_final
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This model is part of the experiments in the published paper at the BabyLM workshop in CoNLL 2023.
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The paper titled "Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure Building" (https://aclanthology.org/2023.conll-babylm.29/)
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<strong>omarmomen/structroberta_s2_final</strong> is a modification on the Roberta Model to incorporate syntactic inductive bias using an unsupervised parsing mechanism.
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This model variant places the parser network after 4 attention blocks.
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The model is pretrained on the BabyLM 10M dataset using a custom pretrained RobertaTokenizer (https://huggingface.co/omarmomen/babylm_tokenizer_32k).
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