# Bert-base-cased Fine Tuned Glue Mrpc Demo

This checkpoint was initialized from the pre-trained checkpoint bert-base-cased and subsequently fine-tuned on GLUE task: mrpc using [this](https://colab.research.google.com/drive/162pW3wonGcMMrGxmA-jdxwy1rhqXd90x?usp=sharing) 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.
{"language": "en", "license": "apache-2.0", "datasets": ["glue"]}
transformers jax bert text-classification en dataset:glue license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #jax #bert #text-classification #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

# Bert-base-cased Fine Tuned Glue Mrpc Demo

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.
# Bert-base-cased Fine Tuned Glue Mrpc Demo

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.
TAGS
#transformers #jax #bert #text-classification #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Bert-base-cased Fine Tuned Glue Mrpc Demo

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.
passage: TAGS
#transformers #jax #bert #text-classification #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# Bert-base-cased Fine Tuned Glue Mrpc Demo

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.