BERT
Collection
BERT models of varying flavors • 22 items • Updated
How to use Intel/bert-large-uncased-squadv1.1-sparse-80-1x4-block-pruneofa with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("question-answering", model="Intel/bert-large-uncased-squadv1.1-sparse-80-1x4-block-pruneofa") # Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("Intel/bert-large-uncased-squadv1.1-sparse-80-1x4-block-pruneofa")
model = AutoModelForQuestionAnswering.from_pretrained("Intel/bert-large-uncased-squadv1.1-sparse-80-1x4-block-pruneofa")This model is a result of fine-tuning a Prune OFA 80% 1x4 block sparse pre-trained BERT-Large combined with knowledge distillation.
This model yields the following results on SQuADv1.1 development set:
{"exact_match": 84.673, "f1": 91.174}
For further details see our paper, Prune Once for All: Sparse Pre-Trained Language Models, and our open source implementation available here.