Longformer-es-mental-base_v2

This model is a fine-tuned version of ELiRF/Longformer-es-mental-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.3324
• Model Preparation Time: 0.0027

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
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 8
• total_train_batch_size: 32
• total_eval_batch_size: 4
• 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
• lr_scheduler_warmup_ratio: 0.06
• num_epochs: 3.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time
1.5397	0.5450	1000	1.4228	0.0027
1.4892	1.0899	2000	1.3839	0.0027
1.466	1.6349	3000	1.3565	0.0027
1.4398	2.1798	4000	1.3333	0.0027
1.4314	2.7248	5000	1.3276	0.0027

Framework versions

• Transformers 4.57.6
• Pytorch 2.6.0+cu118
• Datasets 4.8.5
• Tokenizers 0.22.2