t5-small-gec-new_data

Luffyt/t5-small-gec-new_data

t5-small-gec-new_data is a machine learning model from Luffyt. 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: t5
Access: Public
Created: Dec 3, 2022
Updated: Dec 3, 2022
SHA: 20196d6a

Model card

From the Hugging Face model README

t5-small-gec-new_data

This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4766
Rouge1: 75.2888
Rouge2: 62.2647
Rougel: 75.0714
Rougelsum: 75.0665
Gen Len: 17.3713

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: 1e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
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	Gen Len
1.0361	0.2	50	0.8441	33.973	25.3214	33.6752	33.6214	17.8812
0.9318	0.4	100	0.7763	39.7001	30.2026	39.4182	39.4068	17.8703
0.8131	0.6	150	0.7200	48.3335	37.3238	47.9675	47.9161	17.7565
0.7757	0.8	200	0.6869	56.1066	43.9612	55.7915	55.7617	17.6756
0.7377	1.0	250	0.6532	62.602	49.6693	62.3543	62.3558	17.5729
0.7208	1.2	300	0.6236	67.022	53.6375	66.6774	66.7084	17.52
0.6936	1.39	350	0.6001	69.1363	55.5412	68.8118	68.8147	17.48
0.6537	1.59	400	0.5802	70.7095	57.095	70.4215	70.4339	17.4481
0.6509	1.79	450	0.5648	71.9193	58.2398	71.6452	71.6588	17.4261
0.6417	1.99	500	0.5513	72.5913	58.9961	72.344	72.3608	17.4072
0.6291	2.19	550	0.5371	73.0143	59.5651	72.7976	72.7995	17.3922
0.5976	2.39	600	0.5269	73.5372	60.1145	73.3427	73.3373	17.3743
0.5677	2.59	650	0.5182	73.9687	60.5451	73.7523	73.7587	17.3503
0.6073	2.79	700	0.5112	74.1167	60.7119	73.9078	73.9058	17.3543
0.5904	2.99	750	0.5050	74.3731	61.0562	74.1668	74.1611	17.3513
0.5848	3.19	800	0.4984	74.5208	61.2305	74.3101	74.31	17.3623
0.5627	3.39	850	0.4941	74.7645	61.5633	74.5559	74.5451	17.3633
0.5649	3.59	900	0.4898	74.9032	61.8229	74.6977	74.682	17.3673
0.5524	3.78	950	0.4871	74.9793	61.8885	74.7644	74.7611	17.3613
0.5596	3.98	1000	0.4837	75.1194	62.0917	74.8961	74.8987	17.3723
0.5627	4.18	1050	0.4809	75.2394	62.2355	75.0183	75.0197	17.3683
0.5551	4.38	1100	0.4790	75.2504	62.24	75.0235	75.0216	17.3683
0.5443	4.58	1150	0.4777	75.2608	62.2536	75.0394	75.0403	17.3723
0.5414	4.78	1200	0.4769	75.2812	62.264	75.0614	75.058	17.3723
0.5602	4.98	1250	0.4766	75.2888	62.2647	75.0714	75.0665	17.3713

Framework versions

Transformers 4.25.1
Pytorch 1.12.1+cu113
Datasets 2.7.1
Tokenizers 0.13.2

Library: transformers
Type: t5
License: apache-2.0
Created: Dec 3, 2022
Updated: Dec 3, 2022