swin-aragpt2-image-captioning-v3

AsmaMassad/swin-aragpt2-image-captioning-v3

swin-aragpt2-image-captioning-v3 is a image-text-to-text model from AsmaMassad. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

Downloads · 30 days

4% of all-time downloads

All-time downloads

273

Public

Repo size

5.2 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin1 GB · 100%

At a glance

Task: Image-Text-to-Text
Library: transformers
Model type: vision-encoder-decoder
Access: Public
Created: Jun 6, 2023
Updated: Jun 7, 2023
SHA: ef313948

Try a prompt

Model card

From the Hugging Face model README

swin-aragpt2-image-captioning-v3

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

Loss: 1.1743
Rouge1: 0.0
Rouge2: 0.0
Rougel: 0.0
Rougelsum: 0.0
Meteor: 5.02
Bleu1: 8.5611
Bleu2: 2.9273
Bleu3: 1.1796
Bleu4: 0.4618

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: 1.5e-06
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Meteor	Bleu1	Bleu2	Bleu3	Bleu4
1.5775	4.71	5000	1.2386	1.91	2.6908	1.0804	0.3964	0.1282
1.2446	9.42	10000	1.1985	5.09	8.4549	2.9556	1.2756	0.4817
1.1919	14.12	15000	1.1792	5.4	9.0722	2.9343	1.1887	0.4748
1.1669	18.83	20000	1.1743	5.02	8.5611	2.9273	1.1796	0.4618

Framework versions

Transformers 4.29.2
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3

Task: Image-Text-to-Text
Library: transformers
Type: vision-encoder-decoder
Created: Jun 6, 2023
Updated: Jun 7, 2023