led-base-16384-lfqa

This model is a fine-tuned version of stefanbschneider/led-base-16384-lfqa-ans-len-512 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 3.2615
• Rouge2: 0.0416
• Task: {'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}

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: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Use 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: 1
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge2	Task
3.4849	0.0395	2000	3.4233	0.0387	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.4744	0.0789	4000	3.4411	0.0398	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.4919	0.1184	6000	3.4251	0.0378	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.487	0.1578	8000	3.4200	0.0397	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.4443	0.1973	10000	3.3870	0.0376	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.4597	0.2367	12000	3.3914	0.0405	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.4525	0.2762	14000	3.3845	0.0398	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.4618	0.3156	16000	3.3752	0.0424	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.4573	0.3551	18000	3.3693	0.0421	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.4164	0.3945	20000	3.3640	0.042	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.4125	0.4340	22000	3.3544	0.0412	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.3828	0.4734	24000	3.3423	0.0409	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.3965	0.5129	26000	3.3436	0.0416	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.3993	0.5524	28000	3.3339	0.0384	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.3909	0.5918	30000	3.3122	0.0414	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.3745	0.6313	32000	3.3158	0.0416	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.3665	0.6707	34000	3.3038	0.0424	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.3351	0.7102	36000	3.2915	0.0435	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.3629	0.7496	38000	3.2955	0.0436	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.3465	0.7891	40000	3.2888	0.0395	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.3127	0.8285	42000	3.2800	0.0414	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.3385	0.8680	44000	3.2767	0.0413	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.2882	0.9074	46000	3.2685	0.0437	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.3162	0.9469	48000	3.2639	0.0412	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}
3.3072	0.9863	50000	3.2615	0.0416	{'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}

Framework versions

• Transformers 4.48.3
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0