bert-base-cased-ner

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bert-base-cased-ner

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

• Loss: 0.3793
• Job Title precision: 0.8079
• Job Title recall: 0.8248
• Job Title f1: 0.8163
• Loc precision: 0.8911
• Loc recall: 0.9081
• Loc f1: 0.8995
• Org precision: 0.6484
• Org recall: 0.7620
• Org f1: 0.7006
• Misc precision: 0.6134
• Misc recall: 0.7201
• Misc f1: 0.6625
• Precision: 0.7800
• Recall: 0.8265
• F1: 0.8025
• Accuracy: 0.8606

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Framework versions

• Transformers 4.28.1
• Pytorch 1.7.1+cu110
• Datasets 2.12.0
• Tokenizers 0.13.2