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nickrwu/
distilbert-base-finetuned-mathqa

Name: distilbert-base-finetuned-mathqa
Availability: InStock

generated_from_trainer

distilbert/distilbert-base-uncased

finetune

apache-2.0

endpoints_compatible

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distilbert-base-finetuned-mathqa

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

Loss: 0.1648
Accuracy: 0.9576
F1: 0.9577
Precision: 0.9578
Recall: 0.9576

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: 10
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.2186	1.0	2970	0.1849	0.9455	0.9456	0.9460	0.9454
0.1889	2.0	5940	0.1687	0.9539	0.9540	0.9539	0.9540
0.1528	3.0	8910	0.1648	0.9576	0.9577	0.9578	0.9576

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

nickrwu/distilbert-base-finetuned-mathqa

distilbert-base-finetuned-mathqa

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nickrwu/
distilbert-base-finetuned-mathqa