mDeBERTa-v3-base-mnli-xnli-finetuned-econ_frame-nli-classification

This model is a fine-tuned version of MoritzLaurer/mDeBERTa-v3-base-mnli-xnli on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.3348
• Accuracy: 0.9324
• Accuracy Balanced: 0.9098
• F1 Macro: 0.9191
• F1 Weighted: 0.9316
• F1 Positive: 0.8862
• Precision Positive: 0.925
• Recall Positive: 0.8506
• Precision Weighted: 0.9321
• Recall Weighted: 0.9324
• Mcc: 0.8397
• Roc Auc: 0.9414

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.06
• num_epochs: 3
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Accuracy Balanced	F1 Macro	F1 Weighted	F1 Positive	Precision Positive	Recall Positive	Precision Weighted	Recall Weighted	Mcc	Roc Auc
No log	1.0	164	0.2904	0.9253	0.9173	0.9134	0.9256	0.8814	0.8667	0.8966	0.9262	0.9253	0.8271	0.9373
No log	2.0	328	0.3162	0.9288	0.9041	0.9145	0.9278	0.8795	0.9241	0.8391	0.9286	0.9288	0.8311	0.9548
No log	3.0	492	0.3348	0.9324	0.9098	0.9191	0.9316	0.8862	0.925	0.8506	0.9321	0.9324	0.8397	0.9414

Framework versions

• Transformers 4.57.6
• Pytorch 2.11.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2