distilbert-base-uncased-finetuned-mrpc – AI Model by abhinav-kumar-thakur | AlphaNeural AI

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abhinav-kumar-thakur/
distilbert-base-uncased-finetuned-mrpc

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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distilbert-base-uncased-finetuned-mrpc

This model is a fine-tuned version of distilbert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:

Loss: 0.5556
Accuracy: 0.8578
F1: 0.9007

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	1.0	230	0.3937	0.8113	0.8670
No log	2.0	460	0.3660	0.8480	0.8967
0.4387	3.0	690	0.4298	0.8529	0.8973
0.4387	4.0	920	0.5573	0.8529	0.8990
0.1832	5.0	1150	0.5556	0.8578	0.9007

Framework versions

Transformers 4.20.1
Pytorch 1.11.0+cu113
Datasets 2.3.2
Tokenizers 0.12.1