vmw – AI Model by jaycentg | AlphaNeural AI

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jaycentg/
vmw

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text-classification

generated_from_trainer

google-bert/bert-base-multilingual-uncased

autotrain_compatible

endpoints_compatible

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vmw

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: 0.2897
F1-micro: 0.0
F1-macro: 0.0

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: 15
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	F1-micro	F1-macro
0.4553	1.0	39	0.2936	0.0	0.0
0.2911	2.0	78	0.2904	0.0	0.0
0.2918	3.0	117	0.2904	0.0	0.0
0.288	4.0	156	0.2897	0.0	0.0

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.2.0
Tokenizers 0.19.1