Instructions to use AISE-TUDelft/Custom-Activations-BERT-ReLU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AISE-TUDelft/Custom-Activations-BERT-ReLU with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="AISE-TUDelft/Custom-Activations-BERT-ReLU")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AISE-TUDelft/Custom-Activations-BERT-ReLU") model = AutoModelForMaskedLM.from_pretrained("AISE-TUDelft/Custom-Activations-BERT-ReLU", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
datasets:
- roneneldan/TinyStories
metrics:
- babylm
Basemodel: roBERTa
Configs: Vocab size: 10,000 Hidden size: 512 Max position embeddings: 512 Number of layers: 2 Number of heads: 4 Window size: 256 Intermediate-size: 1024
Results:
- Task: glue Score: 57.91 Confidence Interval: [56.98, 58.87]
- Task: blimp Score: 58.40 Confidence Interval: [57.37, 59.23]