results_t5_small_sciq – AI Model by HenryHoangDuong | AlphaNeural AI

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HenryHoangDuong/
results_t5_small_sciq

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text2text-generation

generated_from_trainer

google-t5/t5-small

text-generation-inference

endpoints_compatible

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results_t5_small_sciq

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

Loss: 0.2182
Rouge1: 0.1381
Rouge2: 0.0193
Rougel: 0.1368
Rougelsum: 0.1369
Bleu: 0.0426
Gen Len: 6.286

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: 0.0001
train_batch_size: 20
eval_batch_size: 20
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Bleu	Gen Len
0.2458	1.0	584	0.2300	0.1314	0.0159	0.1304	0.1303	0.0351	6.22
0.2283	2.0	1168	0.2205	0.13	0.0191	0.1288	0.1292	0.0445	6.23
0.2237	3.0	1752	0.2182	0.1381	0.0193	0.1368	0.1369	0.0426	6.286

Framework versions

Transformers 4.55.0
Pytorch 2.6.0+cu124
Datasets 4.0.0
Tokenizers 0.21.4