deberta-mcq-solver – AI Model by gauravjhaiitm | AlphaNeural AI

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gauravjhaiitm/
deberta-mcq-solver

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microsoft/deberta-v3-base

endpoints_compatible

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deberta_mcq

This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0868
Accuracy: 0.9962
F1 Macro: 0.9960
Map At 3: 0.9981

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: 1e-05
train_batch_size: 8
eval_batch_size: 16
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
lr_scheduler_warmup_ratio: 0.1
num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Map At 3
1.6085	0.9840	46	1.5929	0.6061	0.5985	0.7506
1.3478	1.9893	93	0.7228	0.9242	0.9211	0.9558
0.4559	2.9947	140	0.2588	0.9848	0.9839	0.9924
0.3132	4.0	187	0.1472	0.9924	0.9920	0.9962
0.2326	4.9840	233	0.0957	0.9962	0.9960	0.9981
0.201	5.9037	276	0.0868	0.9962	0.9960	0.9981

Framework versions

Transformers 4.44.2
Pytorch 2.13.0+cu130
Datasets 4.8.5
Tokenizers 0.19.1