SAE-distilbert-base-uncased

jgammack/SAE-distilbert-base-uncased

SAE-distilbert-base-uncased is a fill-mask model from jgammack. Use it when you need the model to fill a missing word. It is set up for transformers. The card lists the license as apache-2.0.

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

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At a glance

Task: Fill-Mask
Library: transformers
License: apache-2.0
Model type: distilbert
Access: Public
Created: Mar 2, 2022
Updated: Feb 9, 2022
SHA: c587ef52

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Model card

From the Hugging Face model README

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

Task: Fill-Mask
Library: transformers
Type: distilbert
License: apache-2.0
Created: Mar 2, 2022
Updated: Feb 9, 2022