output

This model is a fine-tuned version of allenai/longformer-base-4096 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0466
• Name Student Precision: 0.8966
• Name Student Recall: 1.0
• Name Student F1: 0.9455
• Name Student Number: 26
• Overall Precision: 0.8966
• Overall Recall: 1.0
• Overall F1: 0.9455
• Overall Accuracy: 0.9925

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: 2
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 2
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Name Student Precision	Name Student Recall	Name Student F1	Name Student Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.0012	1.0	100	0.0555	0.8966	1.0	0.9455	26	0.8966	1.0	0.9455	0.9925
0.0009	2.0	200	0.0466	0.8966	1.0	0.9455	26	0.8966	1.0	0.9455	0.9925

Framework versions

• Transformers 4.38.1
• Pytorch 2.1.2
• Datasets 2.1.0
• Tokenizers 0.15.2