Structured Pruning Learns Compact and Accurate Models
Paper • 2204.00408 • Published • 1
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("princeton-nlp/CoFi-MNLI-s60")
model = AutoModelForSequenceClassification.from_pretrained("princeton-nlp/CoFi-MNLI-s60")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
This is a model checkpoint for "Structured Pruning Learns Compact and Accurate Models". The model is pruned from bert-base-uncased to a 60% sparsity on dataset MNLI. Please go to our repository for more details on how to use the model for inference. Note that you would have to use the model class specified in our repository to load the model.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="princeton-nlp/CoFi-MNLI-s60")