MoritzLaurer-mDeBERTa-v3-base-mnli-xnli-DottedWSD

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: 0.1333
• Accuracy: 0.9491

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 512
• optimizer: Use adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.1602	0.9997	770	0.1496	0.9379
0.1105	1.9994	1540	0.1295	0.9477
0.1082	2.9990	2310	0.1333	0.9491

Framework versions

• Transformers 4.46.2
• Pytorch 2.5.0+cu121
• Datasets 3.0.1
• Tokenizers 0.20.1