tat – AI Model by jaycentg | AlphaNeural AI

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jaycentg/
tat

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text-classification

generated_from_trainer

google-bert/bert-base-multilingual-uncased

autotrain_compatible

endpoints_compatible

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tat

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

Loss: 0.3164
F1-micro: 0.3349
F1-macro: 0.2353

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: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	F1-micro	F1-macro
0.5547	1.0	25	0.3898	0.0	0.0
0.3766	2.0	50	0.3570	0.0	0.0
0.3383	3.0	75	0.3265	0.2316	0.1630
0.312	4.0	100	0.3164	0.3349	0.2353

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.2.0
Tokenizers 0.19.1