roberta-base-bne-finetuned-detests – AI Model by Pablo94 | AlphaNeural AI

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Pablo94/
roberta-base-bne-finetuned-detests

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text-classification

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BSC-LT/roberta-base-bne

autotrain_compatible

endpoints_compatible

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roberta-base-bne-finetuned-detests

This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.1686
Accuracy: 0.8494
F1-score: 0.7869
Precision: 0.7855
Recall: 0.7883
Auc: 0.7883

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1-score	Precision	Recall	Auc
0.0238	1.0	174	0.6262	0.8543	0.7656	0.8161	0.7382	0.7382
0.0269	2.0	348	1.1233	0.8298	0.6964	0.7997	0.6665	0.6665
0.0003	3.0	522	0.9814	0.8429	0.7600	0.7839	0.7435	0.7435
0.0001	4.0	696	1.1054	0.8445	0.7794	0.7787	0.7801	0.7801
0.0001	5.0	870	1.1088	0.8511	0.7948	0.7865	0.8046	0.8046
0.0001	6.0	1044	1.1265	0.8511	0.7908	0.7873	0.7945	0.7945
0.0001	7.0	1218	1.1441	0.8494	0.7879	0.7852	0.7909	0.7909
0.0	8.0	1392	1.1574	0.8494	0.7869	0.7855	0.7883	0.7883
0.0	9.0	1566	1.1657	0.8494	0.7869	0.7855	0.7883	0.7883
0.0	10.0	1740	1.1686	0.8494	0.7869	0.7855	0.7883	0.7883

Framework versions

Transformers 4.33.1
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3