Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure Building
Paper • 2310.20589 • Published
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("omarmomen/roberta_base_32k_final")
model = AutoModelForMaskedLM.from_pretrained("omarmomen/roberta_base_32k_final")Quick Links
Model Card for omarmomen/roberta_base_32k_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/roberta_base_32k_final is a baseline RobertaModel.
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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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="omarmomen/roberta_base_32k_final")