mt5-summarize-sum

raquelclemente/mt5-summarize-sum

mt5-summarize-sum is a machine learning model from raquelclemente. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.

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

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At a glance

Library: transformers
License: apache-2.0
Model type: mt5
Access: Public
Created: May 12, 2023
Updated: May 13, 2023
SHA: d1234c1f

Model card

From the Hugging Face model README

mt5-summarize-sum

This model is a fine-tuned version of google/mt5-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3984
Rouge1: 0.5736
Rouge2: 0.3783
Rougel: 0.4855
Rougelsum: 0.4844

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: 0.0005
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 90
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
13.8551	0.16	100	5.4672	0.2389	0.0546	0.2119	0.2110
1.0762	0.33	200	0.5982	0.3774	0.2199	0.3493	0.3470
0.8077	0.49	300	0.4999	0.4929	0.3195	0.4349	0.4312
0.7772	0.65	400	0.4652	0.4715	0.3296	0.4431	0.4409
0.7771	0.82	500	0.4402	0.4881	0.3356	0.4433	0.4412
0.713	0.98	600	0.4500	0.4990	0.3291	0.4550	0.4525
0.65	1.15	700	0.4335	0.5522	0.3633	0.4930	0.4909
0.7035	1.31	800	0.4278	0.5227	0.3470	0.4781	0.4772
0.6818	1.47	900	0.4202	0.5325	0.3585	0.4759	0.4744
0.6643	1.64	1000	0.4113	0.5326	0.3486	0.4678	0.4641
0.6007	1.8	1100	0.4122	0.5152	0.3260	0.4572	0.4547
0.5866	1.96	1200	0.4158	0.5538	0.3680	0.4910	0.4903
0.5563	2.13	1300	0.4051	0.5433	0.3371	0.4685	0.4672
0.5727	2.29	1400	0.4089	0.5447	0.3619	0.4711	0.4695
0.5859	2.45	1500	0.4033	0.5464	0.3411	0.4688	0.4662
0.5783	2.62	1600	0.3997	0.5667	0.3595	0.4825	0.4787
0.5673	2.78	1700	0.3992	0.5759	0.3882	0.4911	0.4891
0.57	2.95	1800	0.3984	0.5736	0.3783	0.4855	0.4844

Framework versions

Transformers 4.27.4
Pytorch 1.13.0
Datasets 2.1.0
Tokenizers 0.13.2

Library: transformers
Type: mt5
License: apache-2.0
Created: May 12, 2023
Updated: May 13, 2023