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

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lct-rug-2022/
edos-2023-baseline-bert-base-multilingual-uncased-label_vector

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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edos-2023-baseline-bert-base-multilingual-uncased-label_vector

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

Loss: 1.6113
F1: 0.2785

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
2.0125	1.18	100	1.8290	0.1089
1.6698	2.35	200	1.6458	0.2223
1.4812	3.53	300	1.6035	0.2463
1.3137	4.71	400	1.5729	0.2502
1.2143	5.88	500	1.5549	0.2697
1.0805	7.06	600	1.5553	0.2759
0.9838	8.24	700	1.5730	0.2879
0.8981	9.41	800	1.6113	0.2785

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.7.1
Tokenizers 0.13.2