nyu-mll/glue
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How to use patrickvonplaten/bert-base-cased_fine_tuned_glue_mrpc_demo with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="patrickvonplaten/bert-base-cased_fine_tuned_glue_mrpc_demo") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/bert-base-cased_fine_tuned_glue_mrpc_demo")
model = AutoModelForSequenceClassification.from_pretrained("patrickvonplaten/bert-base-cased_fine_tuned_glue_mrpc_demo", device_map="auto")This checkpoint was initialized from the pre-trained checkpoint bert-base-cased and subsequently fine-tuned on GLUE task: mrpc using this notebook. Training was conducted for 3 epochs, using a linear decaying learning rate of 2e-05, and a total batch size of 32.
The model has a final training loss of 0.103 and a accuracy of 0.831.