tibetan-code-switching-detector

This model is a fine-tuned version of OMRIDRORI/mbert-tibetan-continual-unicode-240k on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.7828
• Accuracy: 0.8124
• Proximity F1: 0.0772
• Proximity Recall: 0.2920
• Proximity Precision: 0.0457
• Exact Matches: 0.7963
• Missed Switches: 0.0556
• False Switches: 14.7685
• Matches At 1 Words: 0.0093
• Matches At 2 Words: 0.0
• Matches At 3 Words: 0.0
• Matches At 4 Words: 0.0
• Matches At 5 Words: 0.0093
• Matches At 6 Words: 0.0
• Matches At 7 Words: 0.0
• Matches At 8 Words: 0.0
• Matches At 9 Words: 0.0
• Matches At 10 Words: 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: 1e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Proximity F1	Proximity Recall	Proximity Precision	Exact Matches	Missed Switches	False Switches	Matches At 1 Words	Matches At 2 Words	Matches At 3 Words	Matches At 4 Words	Matches At 5 Words	Matches At 6 Words	Matches At 7 Words	Matches At 8 Words	Matches At 9 Words	Matches At 10 Words
1.4889	4.5977	200	0.9309	0.8405	0.1133	0.1649	0.0959	0.3981	0.3611	4.5741	0.0093	0.0	0.0	0.0093	0.0185	0.0185	0.0	0.0	0.0556	0.0
0.8272	9.1954	400	0.7828	0.8124	0.0772	0.2920	0.0457	0.7963	0.0556	14.7685	0.0093	0.0	0.0	0.0	0.0093	0.0	0.0	0.0	0.0	0.0

Framework versions

• Transformers 4.46.3
• Pytorch 2.4.1+cu121
• Datasets 2.0.0
• Tokenizers 0.20.3