QA_FineTuned_DistilBert-based-uncased – AI Model by gp-tar4 | AlphaNeural AI

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gp-tar4/
QA_FineTuned_DistilBert-based-uncased

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distilbert/distilbert-base-cased

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distil_final

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

Loss: 3.5805

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: 0.0002
train_batch_size: 10
eval_batch_size: 10
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 40
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
5.0926	0.32	10	3.9348
3.9237	0.64	20	4.0141
3.8084	0.96	30	3.8194
3.6561	1.28	40	3.6860
3.4836	1.6	50	3.6023
3.5731	1.92	60	3.5904
3.497	2.24	70	3.6435
3.34	2.56	80	3.5359
3.2943	2.88	90	3.6003
3.0902	3.2	100	3.6634
3.1061	3.52	110	3.6059
3.1395	3.84	120	3.5805

Framework versions

Transformers 4.40.0
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.19.1