Instructions to use omarmomen/structroberta_s1_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omarmomen/structroberta_s1_final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="omarmomen/structroberta_s1_final", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("omarmomen/structroberta_s1_final", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("omarmomen/structroberta_s1_final", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Model Card for omarmomen/structroberta_s1_final
This model is part of the experiments in the published paper at the BabyLM workshop in CoNLL 2023. The paper titled "Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure Building" (https://aclanthology.org/2023.conll-babylm.29/)
omarmomen/structroberta_s1_final is a modification on the Roberta Model to incorporate syntactic inductive bias using an unsupervised parsing mechanism.
This model variant places the parser network ahead of all attention blocks.
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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Dataset used to train omarmomen/structroberta_s1_final
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