t5-cnndm

ryusangwon/t5-cnndm

t5-cnndm is a machine learning model from ryusangwon. 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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Hugging Face

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At a glance

Library: transformers
License: apache-2.0
Model type: t5
Access: Public
Created: Feb 10, 2024
Updated: Feb 11, 2024
SHA: bdd16847

Base models

google-t5/t5-base

Model card

From the Hugging Face model README

cnn_dailymail_350_t5-base

This model is a fine-tuned version of google-t5/t5-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.8973
Rouge1: 0.2524
Rouge2: 0.1238
Rougel: 0.2084
Rougelsum: 0.2083
Gen Len: 18.9993

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: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
0.8756	0.45	500	0.9285	0.2483	0.1206	0.2051	0.2051	18.9993
0.8719	0.89	1000	0.9147	0.2496	0.1221	0.2063	0.2062	18.9999
0.8407	1.34	1500	0.9101	0.2497	0.1217	0.2061	0.2061	18.9999
0.8433	1.78	2000	0.9054	0.2512	0.1225	0.2072	0.2072	18.9995
0.8346	2.23	2500	0.9048	0.2515	0.123	0.2074	0.2074	18.9998
0.8308	2.67	3000	0.9037	0.2504	0.1226	0.2073	0.2073	18.9996
0.8189	3.12	3500	0.9022	0.2517	0.1232	0.2082	0.2081	19.0
0.8275	3.57	4000	0.9011	0.2514	0.123	0.2076	0.2076	19.0
0.8272	4.01	4500	0.9010	0.2517	0.1236	0.2081	0.2081	18.9993
0.819	4.46	5000	0.8994	0.2517	0.1235	0.208	0.2079	18.999
0.8096	4.9	5500	0.9001	0.2518	0.1236	0.208	0.208	18.9992
0.823	5.35	6000	0.8976	0.2519	0.1232	0.208	0.208	18.9993
0.8205	5.8	6500	0.8979	0.2516	0.1234	0.2079	0.2079	18.9996
0.8136	6.24	7000	0.8981	0.2515	0.1232	0.2078	0.2078	18.9992
0.8117	6.69	7500	0.8984	0.2519	0.1236	0.2081	0.208	18.9996
0.8039	7.13	8000	0.8979	0.2524	0.1237	0.2083	0.2083	18.9993
0.7934	7.58	8500	0.8981	0.2517	0.1235	0.2078	0.2078	18.9992
0.7947	8.02	9000	0.8979	0.252	0.1237	0.2081	0.2081	18.9989
0.8189	8.47	9500	0.8974	0.2523	0.1237	0.2083	0.2083	18.999
0.8102	8.92	10000	0.8976	0.2523	0.1237	0.2084	0.2084	18.9991
0.8029	9.36	10500	0.8978	0.2523	0.1237	0.2083	0.2083	18.9992
0.8004	9.81	11000	0.8973	0.2524	0.1238	0.2084	0.2083	18.9993

Framework versions

Transformers 4.36.2
Pytorch 2.0.1+cu117
Datasets 2.15.0
Tokenizers 0.15.0

Library: transformers
Type: t5
License: apache-2.0
Created: Feb 10, 2024
Updated: Feb 11, 2024