roberta-base-downstream-build_rr – AI Model by MHGanainy | AlphaNeural AI

You can deploy this model and start earning money today!

MHGanainy/
roberta-base-downstream-build_rr

like

0

token-classification

generated_from_trainer

FacebookAI/roberta-base

autotrain_compatible

endpoints_compatible

Views

No views yet

roberta-base-downstream-build_rr

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

Loss: 0.8610
Precision-macro: 0.6015
Recall-macro: 0.5642
Macro-f1: 0.5742
Precision-micro: 0.7871
Recall-micro: 0.7871
Micro-f1: 0.7871
Accuracy: 0.7871

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: 3e-05
train_batch_size: 2
eval_batch_size: 2
seed: 1
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision-macro	Recall-macro	Macro-f1	Precision-micro	Recall-micro	Micro-f1	Accuracy
No log	1.0	124	0.9703	0.5485	0.3447	0.3566	0.7155	0.7155	0.7155	0.7155
No log	2.0	248	0.8005	0.5181	0.5222	0.5080	0.7353	0.7353	0.7353	0.7353
No log	3.0	372	0.8156	0.5626	0.5322	0.5288	0.7454	0.7454	0.7454	0.7454
No log	4.0	496	0.7056	0.5881	0.5197	0.5180	0.7704	0.7704	0.7704	0.7704
1.0549	5.0	620	0.7526	0.5878	0.5906	0.5775	0.7642	0.7642	0.7642	0.7642
1.0549	6.0	744	0.7094	0.6336	0.5395	0.5649	0.7812	0.7812	0.7812	0.7812
1.0549	7.0	868	0.7391	0.6475	0.5339	0.5535	0.7808	0.7808	0.7808	0.7808
1.0549	8.0	992	0.7354	0.6169	0.5756	0.5881	0.7930	0.7930	0.7930	0.7930
0.545	9.0	1116	0.8143	0.5951	0.5963	0.5928	0.7805	0.7805	0.7805	0.7805
0.545	10.0	1240	0.8352	0.6029	0.5915	0.5918	0.7794	0.7794	0.7794	0.7794
0.545	11.0	1364	0.8610	0.6015	0.5642	0.5742	0.7871	0.7871	0.7871	0.7871

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1