NASca-finetuned-diego-2

dtorber/NASca-finetuned-diego-2

NASca-finetuned-diego-2 is a summarization model from dtorber. Use it when you need a shorter version of a longer text. It is set up for transformers.

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

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At a glance

Task: Summarization
Library: transformers
Model type: bart
Access: Public
Created: Feb 22, 2023
Updated: Feb 25, 2023
SHA: 3bc232b0

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Model card

From the Hugging Face model README

NASca-finetuned-diego-2

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

Loss: 1.9197
Rouge1: 0.2930
Rouge2: 0.1226
Rougel: 0.2327
Rougelsum: 0.2373

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: 1.3739167643078955e-06
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
1.4353	0.09	15000	1.7848	0.2816	0.1157	0.2258	0.2305
1.3237	1.0	159149	1.7707	0.2831	0.1171	0.2267	0.2318
1.3836	2.0	318298	1.7924	0.2899	0.1211	0.2316	0.2361
1.274	3.0	477447	1.8809	0.2921	0.1225	0.2323	0.2368
1.2289	4.0	636596	1.9128	0.2926	0.1223	0.2322	0.2368
1.2424	5.0	795745	1.9197	0.2930	0.1226	0.2327	0.2373

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu117
Datasets 2.9.0
Tokenizers 0.13.2

Task: Summarization
Library: transformers
Type: bart
Created: Feb 22, 2023
Updated: Feb 25, 2023