scenario-TCR-data-glue-qnli-model-bert-base-uncased – AI Model by haryoaw | AlphaNeural AI

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haryoaw/
scenario-TCR-data-glue-qnli-model-bert-base-uncased

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text-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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scenario-TCR-data-glue-qnli-model-bert-base-uncased

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

Loss: 0.4691
Accuracy: 0.8885

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: 5e-05
train_batch_size: 8
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 6969

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.303	1.0	3273	0.2478	0.9023
0.2095	2.0	6546	0.2681	0.9033
0.1365	3.0	9819	0.3354	0.8993
0.1107	4.0	13093	0.3548	0.8971
0.0963	5.0	16366	0.4401	0.8922
0.0862	6.0	19639	0.4351	0.8876
0.0818	7.0	22912	0.4691	0.8885

Framework versions

Transformers 4.36.2
Pytorch 2.1.2
Datasets 2.16.0
Tokenizers 0.15.0