README.md

metadata

tags:
  - generated_from_trainer
metrics:
  - accuracy
  - recall
  - precision
model-index:
  - name: MARBERT-QADI
    results: []

MARBERT-QADI

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.0342
• Macro F1: 0.5099
• Accuracy: 0.5138
• Recall: 0.5136
• Precision: 0.6223

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: 4e-06
• train_batch_size: 128
• eval_batch_size: 128
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Macro F1	Accuracy	Recall	Precision
0.8588	1.0	1125	0.7883	0.7550	0.7554	0.7552	0.7609
0.7475	2.0	2250	0.7718	0.7632	0.7634	0.7631	0.7653
0.6527	3.0	3375	0.7758	0.7668	0.7673	0.7671	0.7679
0.5654	4.0	4500	0.7845	0.7665	0.7673	0.7671	0.7682
0.5001	5.0	5625	0.8068	0.7650	0.7663	0.7660	0.7657
0.4641	6.0	6750	0.8216	0.7647	0.7658	0.7655	0.7650
0.4049	7.0	7875	0.8393	0.7645	0.7654	0.7649	0.7657
0.3773	8.0	9000	0.8477	0.7651	0.7657	0.7654	0.7659
0.3393	9.0	10125	0.8569	0.7663	0.7669	0.7665	0.7670
0.3383	10.0	11250	0.8589	0.7663	0.7669	0.7666	0.7667

Framework versions

• Transformers 4.40.1
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.19.1