en-multinerd-ner-unmasked-upsampled

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en-multinerd-ner-unmasked-upsampled

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.5953
• Precision: 0.8771
• Recall: 0.7115
• F1: 0.7857
• Accuracy: 0.9634
• Per-precision: 0.9944
• Per-recall: 0.9959
• Per-f1: 0.9951
• Org-precision: 0.9328
• Org-recall: 0.9434
• Org-f1: 0.9381
• Loc-precision: 0.9697
• Loc-recall: 0.9736
• Loc-f1: 0.9717
• Dis-precision: 0.0
• Dis-recall: 0.0
• Dis-f1: 0.0
• Anim-precision: 0.6852
• Anim-recall: 0.7293
• Anim-f1: 0.7065

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: 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 4.36.1
• Pytorch 2.1.0+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0