SAE-distilbert-base-uncased – AI Model by jgammack | AlphaNeural AI

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jgammack/
SAE-distilbert-base-uncased

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SAE-distilbert-base-uncased

This model is a fine-tuned version of distilbert-base-uncased on the jgammack/SAE-door-abstracts dataset.

It achieves the following results on the evaluation set:

Loss: 2.2970

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 15
eval_batch_size: 15
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
2.5323	1.0	37	2.4503
2.4968	2.0	74	2.4571
2.4688	3.0	111	2.4099
2.419	4.0	148	2.3343
2.4229	5.0	185	2.3072
2.4067	6.0	222	2.2927
2.3877	7.0	259	2.2836
2.374	8.0	296	2.3767
2.3582	9.0	333	2.2493
2.356	10.0	370	2.2847
2.3294	11.0	407	2.3234
2.3358	12.0	444	2.2660
2.3414	13.0	481	2.2887
2.3154	14.0	518	2.3737
2.311	15.0	555	2.2686

Framework versions

Transformers 4.16.2
Pytorch 1.10.0+cu111
Datasets 1.18.3
Tokenizers 0.11.0