t5-small-xsum – AI Model by adasnew | AlphaNeural AI

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t5-small-xsum

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text2text-generation

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text-generation-inference

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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