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roequitz/
t5-abs-2309-1054-lr-1e-05-bs-10-maxep-20

Name: t5-abs-2309-1054-lr-1e-05-bs-10-maxep-20
Availability: InStock

safetensors

generated_from_trainer

google-t5/t5-base

finetune

apache-2.0

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t5-abs-2309-1054-lr-1e-05-bs-10-maxep-20

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

Loss: 4.0044
Rouge/rouge1: 0.4791
Rouge/rouge2: 0.2351
Rouge/rougel: 0.4085
Rouge/rougelsum: 0.4098
Bertscore/bertscore-precision: 0.8984
Bertscore/bertscore-recall: 0.8999
Bertscore/bertscore-f1: 0.899
Meteor: 0.447
Gen Len: 41.7727

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: 10
eval_batch_size: 10
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 20
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge/rouge1	Rouge/rouge2	Rouge/rougel	Rouge/rougelsum	Bertscore/bertscore-precision	Bertscore/bertscore-recall	Bertscore/bertscore-f1	Meteor	Gen Len
0.0089	0.9885	43	3.9871	0.4806	0.2393	0.4125	0.4129	0.8987	0.8999	0.8991	0.4493	41.7
0.0062	2.0	87	3.9921	0.4805	0.2395	0.4119	0.4124	0.8984	0.8997	0.8989	0.4488	41.6727
0.0046	2.9885	130	3.9973	0.4806	0.2358	0.4101	0.4109	0.8984	0.8993	0.8988	0.448	41.2
0.0046	4.0	174	4.0023	0.4787	0.2353	0.4084	0.4095	0.8989	0.899	0.8988	0.4445	40.8273
0.0051	4.9885	217	4.0062	0.4817	0.2381	0.4116	0.4125	0.8996	0.8992	0.8993	0.4456	40.5455
0.0044	6.0	261	4.0107	0.4796	0.2351	0.4089	0.4099	0.8994	0.8988	0.8989	0.4423	40.2727
0.0046	6.9885	304	4.0121	0.4795	0.2331	0.4083	0.409	0.8991	0.8986	0.8987	0.4393	40.1455
0.0043	8.0	348	4.0119	0.4799	0.2345	0.4086	0.4092	0.899	0.899	0.8989	0.4426	40.6909
0.0043	8.9885	391	4.0124	0.4778	0.2344	0.4076	0.4083	0.899	0.8988	0.8988	0.4402	40.5364
0.0038	10.0	435	4.0146	0.4791	0.2349	0.4087	0.4096	0.8992	0.8991	0.899	0.4413	40.6909
0.0113	10.9885	478	4.0149	0.4794	0.2361	0.4088	0.4096	0.8985	0.8992	0.8987	0.4436	41.2091
0.0116	12.0	522	4.0099	0.4817	0.2387	0.4112	0.4119	0.8986	0.8997	0.899	0.4482	41.5545
0.0116	12.9885	565	4.0083	0.4811	0.2378	0.411	0.4119	0.8992	0.8997	0.8993	0.4472	41.3636
0.0109	14.0	609	4.0073	0.4804	0.2363	0.41	0.4108	0.899	0.8998	0.8993	0.4462	41.4364
0.0109	14.9885	652	4.0056	0.4796	0.2362	0.409	0.4096	0.8987	0.9	0.8992	0.4476	41.7636
0.0107	16.0	696	4.0045	0.4796	0.2353	0.4095	0.4098	0.8988	0.8998	0.8991	0.4471	41.4727
0.0117	16.9885	739	4.0039	0.4789	0.234	0.4076	0.4084	0.8992	0.8997	0.8993	0.4455	41.2455
0.0103	18.0	783	4.0045	0.4785	0.2342	0.4078	0.4088	0.8986	0.8996	0.899	0.4452	41.4909
0.0111	18.9885	826	4.0044	0.4776	0.2339	0.4069	0.408	0.8986	0.8998	0.8991	0.4456	41.6
0.0119	19.7701	860	4.0044	0.4791	0.2351	0.4085	0.4098	0.8984	0.8999	0.899	0.447	41.7727

Framework versions

Transformers 4.44.0
Pytorch 2.4.0
Datasets 2.21.0
Tokenizers 0.19.1

roequitz/t5-abs-2309-1054-lr-1e-05-bs-10-maxep-20

t5-abs-2309-1054-lr-1e-05-bs-10-maxep-20

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

roequitz/
t5-abs-2309-1054-lr-1e-05-bs-10-maxep-20