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darija-ner-xlmroberta – AI Model by ElAtrachAMINE | AlphaNeural AI
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darija-ner-xlmroberta
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transformers
safetensors
xlm-roberta
token-classification
generated_from_trainer
FacebookAI/xlm-roberta-base
finetune
mit
endpoints_compatible
us
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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
