t5-small-xsum

adasnew/t5-small-xsum

t5-small-xsum is a machine learning model from adasnew. 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: Apr 11, 2022
Updated: Apr 11, 2022
SHA: 58fcedfd

Datasets

xsum

Model card

From the Hugging Face model README

t5-small-xsum

This model is a fine-tuned version of t5-small on the xsum dataset. It achieves the following results on the evaluation set:

Loss: 2.3953

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

Training results

Training Loss	Epoch	Step	Validation Loss
2.8641	0.04	500	2.6202
2.7466	0.08	1000	2.5660
2.8767	0.12	1500	2.5319
2.7099	0.16	2000	2.5107
2.7752	0.2	2500	2.4922
2.6037	0.24	3000	2.4800
2.8236	0.27	3500	2.4677
2.7089	0.31	4000	2.4581
2.7299	0.35	4500	2.4498
2.7498	0.39	5000	2.4420
2.6186	0.43	5500	2.4346
2.7817	0.47	6000	2.4288
2.5559	0.51	6500	2.4239
2.6725	0.55	7000	2.4186
2.6316	0.59	7500	2.4149
2.5561	0.63	8000	2.4115
2.5708	0.67	8500	2.4097
2.5861	0.71	9000	2.4052
2.6363	0.74	9500	2.4024
2.7435	0.78	10000	2.4003
2.7258	0.82	10500	2.3992
2.6113	0.86	11000	2.3983
2.6006	0.9	11500	2.3972
2.5684	0.94	12000	2.3960
2.6181	0.98	12500	2.3953

Framework versions

Transformers 4.18.0
Pytorch 1.10.0+cu111
Datasets 2.0.0
Tokenizers 0.11.6

Library: transformers
Type: t5
License: apache-2.0
Created: Apr 11, 2022
Updated: Apr 11, 2022