mt5-small

mqy/mt5-small

mt5-small is a summarization model from mqy. Use it when you need a shorter version of a longer text. 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. --

Downloads · 30 days

6% of all-time downloads

All-time downloads

222

Public

Repo size

27.6 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin1.2 GB · 98%

At a glance

Task: Summarization
Library: transformers
License: apache-2.0
Model type: mt5
Access: Public
Created: Mar 5, 2023
Updated: Mar 6, 2023
SHA: 4743b8ac

Try a prompt

Model card

From the Hugging Face model README

mt5-small

This model is a fine-tuned version of google/mt5-small on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.3524
Rouge1: 21.18
Rouge2: 6.37
Rougel: 20.84

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: 0.0001
train_batch_size: 9
eval_batch_size: 9
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel
4.6211	1.45	500	2.5968	16.96	4.86	16.73
3.1269	2.9	1000	2.4790	17.62	5.0	17.58
2.884	4.35	1500	2.4077	17.67	5.06	17.4
2.7627	5.8	2000	2.4003	18.67	5.42	18.26
2.638	7.25	2500	2.3953	18.76	5.49	18.44
2.5427	8.7	3000	2.3837	18.97	6.04	18.62
2.4846	10.14	3500	2.3957	20.17	6.23	19.88
2.3867	11.59	4000	2.3558	19.5	6.24	19.1
2.3651	13.04	4500	2.3225	19.6	6.18	19.2
2.2846	14.49	5000	2.3385	19.34	6.3	18.9
2.2351	15.94	5500	2.3413	20.42	6.44	19.93
2.1862	17.39	6000	2.3418	20.04	6.35	19.51
2.1375	18.84	6500	2.3438	21.02	6.56	20.45
2.0961	20.29	7000	2.3451	20.82	6.81	20.6
2.0686	21.74	7500	2.3571	20.46	6.57	20.03
2.0253	23.19	8000	2.3672	20.49	6.21	20.16
1.9997	24.64	8500	2.3524	21.18	6.37	20.84
1.9627	26.09	9000	2.3780	20.9	5.96	20.4
1.9561	27.54	9500	2.3808	21.06	6.59	20.76
1.902	28.99	10000	2.3739	20.73	6.09	20.41
1.8837	30.43	10500	2.3786	20.65	6.27	20.35
1.8587	31.88	11000	2.3853	20.44	6.23	20.0

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2

Task: Summarization
Library: transformers
Type: mt5
License: apache-2.0
Created: Mar 5, 2023
Updated: Mar 6, 2023