edos-2023-baseline-bert-base-uncased-label_category – AI Model by lct-rug-2022 | AlphaNeural AI

You can deploy this model and start earning money today!

lct-rug-2022/
edos-2023-baseline-bert-base-uncased-label_category

like

0

text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

Views

No views yet

edos-2023-baseline-bert-base-uncased-label_category

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

Loss: 1.0354
F1: 0.5675

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: 1e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 5
num_epochs: 12
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1
1.1743	1.18	100	1.1120	0.1949
1.0197	2.35	200	1.0548	0.3307
0.8872	3.53	300	0.9621	0.4795
0.7117	4.71	400	0.9876	0.4947
0.6173	5.88	500	0.9615	0.5447
0.5015	7.06	600	0.9973	0.5512
0.4076	8.24	700	1.0052	0.5620
0.3381	9.41	800	1.0354	0.5675

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.7.1
Tokenizers 0.13.2