• generated_from_trainer datasets:
• ner-tr metrics:
• precision
• recall
• f1
• accuracy model-index:
• name: named-entity-tr results:
  • task: name: Token Classification type: token-classification dataset: name: ner-tr type: ner-tr config: NERTR split: train args: NERTR metrics:
    • name: Precision type: precision value: 0.0
    • name: Recall type: recall value: 0.0
    • name: F1 type: f1 value: 0.0
    • name: Accuracy type: accuracy value: 0.45027322404371584

named-entity-tr

• Loss: 2.2782
• Precision: 0.0
• Recall: 0.0
• F1: 0.0
• Accuracy: 0.4503

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Framework versions

• Transformers 4.22.0
• Pytorch 1.12.1+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1