BERTtime-Stories
Collection
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How to use nikitastheo/BERTtime-Stories-100m-nucleus-1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("fill-mask", model="nikitastheo/BERTtime-Stories-100m-nucleus-1", trust_remote_code=True) # pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("nikitastheo/BERTtime-Stories-100m-nucleus-1", trust_remote_code=True, device_map="auto")This model uses the LTG-BERT architecture. The model was trained on a combination of the BabyLM Dataset, the TinyStories Dataset, and generated data, in accordance with the rules of the Stric track, and the 100M word budget.
The model was trained with 128 token sequence length
Hyperparameters used and evaluation scores will follow in a subsequent update.