T5_sum_challenge2

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

• Loss: nan
• Rouge1: 0.2177
• Rouge2: 0.063
• Rougel: 0.167
• Rougelsum: 0.1689
• Gen Len: 20.0
• Bleu: 0.0246
• Precisions: 0.0879
• Brevity Penalty: 0.5266
• Length Ratio: 0.6093
• Translation Length: 736.0
• Reference Length: 1208.0
• Precision: 0.8576
• Recall: 0.8527
• F1: 0.8551
• Hashcode: roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4)

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: 2e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 16
• 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
• num_epochs: 4
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len	Bleu	Precisions	Brevity Penalty	Length Ratio	Translation Length	Reference Length	Precision	Recall	F1	Hashcode
No log	1.0	7	nan	0.2177	0.063	0.167	0.1689	20.0	0.0246	0.0879	0.5266	0.6093	736.0	1208.0	0.8576	0.8527	0.8551	roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4)
No log	2.0	14	nan	0.2177	0.063	0.167	0.1689	20.0	0.0246	0.0879	0.5266	0.6093	736.0	1208.0	0.8576	0.8527	0.8551	roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4)
No log	3.0	21	nan	0.2177	0.063	0.167	0.1689	20.0	0.0246	0.0879	0.5266	0.6093	736.0	1208.0	0.8576	0.8527	0.8551	roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4)
No log	4.0	28	nan	0.2177	0.063	0.167	0.1689	20.0	0.0246	0.0879	0.5266	0.6093	736.0	1208.0	0.8576	0.8527	0.8551	roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4)

Framework versions

• Transformers 4.52.4
• Pytorch 2.7.0+cu126
• Datasets 3.6.0
• Tokenizers 0.21.1