fine_tuned_distilbert-base-uncased – AI Model by Chessmen | AlphaNeural AI

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Chessmen/
fine_tuned_distilbert-base-uncased

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generated_from_trainer

stanfordnlp/imdb

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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fine_tune_distilbert-base-uncased

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

Loss: 2.1226
Model Preparation Time: 0.0016

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: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time
2.5551	1.0	767	2.3648	0.0016
2.4329	2.0	1534	2.3181	0.0016
2.3874	3.0	2301	2.2831	0.0016
2.3409	4.0	3068	2.2422	0.0016
2.3124	5.0	3835	2.2302	0.0016
2.2895	6.0	4602	2.2104	0.0016
2.2649	7.0	5369	2.2014	0.0016
2.2445	8.0	6136	2.1939	0.0016
2.234	9.0	6903	2.1776	0.0016
2.2142	10.0	7670	2.1607	0.0016
2.208	11.0	8437	2.1682	0.0016
2.1933	12.0	9204	2.1530	0.0016
2.1808	13.0	9971	2.1493	0.0016
2.1689	14.0	10738	2.1422	0.0016
2.1598	15.0	11505	2.1347	0.0016
2.1567	16.0	12272	2.1373	0.0016
2.1458	17.0	13039	2.1270	0.0016
2.1475	18.0	13806	2.1200	0.0016
2.141	19.0	14573	2.1312	0.0016
2.1423	20.0	15340	2.1202	0.0016

Framework versions

Transformers 4.44.2
Pytorch 2.2.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1