NLP_whole_dataseet_

This model is a fine-tuned version of google-bert/bert-base-multilingual-uncased on the None dataset. It achieves the following results on the evaluation set:

• eval_loss: 0.0225
• eval_accuracy: 0.9954
• eval_precision: 0.9951
• eval_recall: 0.9960
• eval_f1: 0.9955
• eval_runtime: 0.792
• eval_samples_per_second: 275.256
• eval_steps_per_second: 8.839
• epoch: 5.0
• step: 275

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: cosine
• num_epochs: 8

Framework versions

• Transformers 4.42.4
• Pytorch 2.3.1+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1