darija-ner-xlmroberta

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darija-ner-xlmroberta

This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:

• eval_loss: 1.1109
• eval_precision: 0.75
• eval_recall: 0.7627
• eval_f1: 0.7563
• eval_accuracy: 0.8541
• eval_BRAND_f1: 0.6857
• eval_BRAND_precision: 0.6316
• eval_BRAND_recall: 0.75
• eval_CITY_f1: 1.0
• eval_CITY_precision: 1.0
• eval_CITY_recall: 1.0
• eval_COLOR_f1: 0.0
• eval_COLOR_precision: 0.0
• eval_COLOR_recall: 0.0
• eval_PRICE_f1: 0.64
• eval_PRICE_precision: 0.5714
• eval_PRICE_recall: 0.7273
• eval_PRODUCT_f1: 0.8837
• eval_PRODUCT_precision: 0.9048
• eval_PRODUCT_recall: 0.8636
• eval_runtime: 0.3558
• eval_samples_per_second: 123.664
• eval_steps_per_second: 16.863
• epoch: 10.0
• step: 1130

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: 3e-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_steps: 224
• num_epochs: 20
• mixed_precision_training: Native AMP

Framework versions

• Transformers 5.12.0
• Pytorch 2.11.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2