shengqin
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bert-seq

Text Classification

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text-embeddings-inference

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bert-seq

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

Loss: 0.0087
Accuracy: 0.9988

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: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0127	1.0	3697	0.0072	0.9986
0.0009	2.0	7394	0.0087	0.9988

Framework versions

Transformers 4.33.3
Pytorch 2.0.1
Datasets 2.14.6
Tokenizers 0.13.3

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Base model

google-bert/bert-base-multilingual-cased

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