distilbert-qa-checkpoint-v3 – AI Model by Eitanli | AlphaNeural AI

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distilbert-qa-checkpoint-v3

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distilbert-qa-checkpoint-v3

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4014

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: 30

Training results

Training Loss	Epoch	Step	Validation Loss
0.5057	1.0	662	0.1916
0.1801	2.0	1324	0.1784
0.1295	3.0	1986	0.1688
0.084	4.0	2648	0.1876
0.0748	5.0	3310	0.2192
0.0621	6.0	3972	0.1994
0.0488	7.0	4634	0.2558
0.0462	8.0	5296	0.2264
0.046	9.0	5958	0.2734
0.038	10.0	6620	0.2966
0.0325	11.0	7282	0.2773
0.0341	12.0	7944	0.3233
0.0297	13.0	8606	0.3062
0.0293	14.0	9268	0.3455
0.0255	15.0	9930	0.3306
0.0228	16.0	10592	0.3239
0.0248	17.0	11254	0.3393
0.0216	18.0	11916	0.4004
0.023	19.0	12578	0.3621
0.0175	20.0	13240	0.3544
0.0227	21.0	13902	0.3986
0.0185	22.0	14564	0.3904
0.0194	23.0	15226	0.3922
0.0178	24.0	15888	0.4096
0.0177	25.0	16550	0.4134
0.0183	26.0	17212	0.3971
0.0194	27.0	17874	0.4049
0.0184	28.0	18536	0.3987
0.0171	29.0	19198	0.4012
0.0169	30.0	19860	0.4014

Framework versions

Transformers 4.27.2
Pytorch 1.13.1+cu117
Datasets 2.11.0
Tokenizers 0.13.3