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bertweet-base_wnut17_ner

Token Classification
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PyTorch
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roberta
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named-entity-recognition
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - wnut_17
model-index:
  - name: fine_tune_bert_output
    results: []

Bertweet-base finetuned on wnut17_ner

This model is a fine-tuned version of vinai/bertweet-base on the wnut_17 dataset.

It achieves the following results on the evaluation set:

  • Loss: 0.3239
  • Overall Precision: 0.6913
  • Overall Recall: 0.5914
  • Overall F1: 0.6374
  • Overall Accuracy: 0.9499
  • Corporation F1: 0.2703
  • Creative-work F1: 0.3636
  • Group F1: 0.4030
  • Location F1: 0.7500
  • Person F1: 0.7733
  • Product F1: 0.4152

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: 100

Training results

Training Loss Epoch Step Validation Loss Overall Precision Overall Recall Overall F1 Overall Accuracy Corporation F1 Creative-work F1 Group F1 Location F1 Person F1 Product F1
0.2691 1.0 213 0.4035 0.0 0.0 0.0 0.8979 0.0 0.0 0.0 0.0 0.0 0.0
0.1604 2.0 426 0.3054 0.6255 0.4161 0.4998 0.9324 0.0 0.0 0.0 0.3534 0.6877 0.0
0.1118 3.0 639 0.2864 0.6655 0.4643 0.5470 0.9404 0.1961 0.1164 0.1538 0.5803 0.7221 0.1865
0.0524 4.0 852 0.2891 0.6945 0.5042 0.5842 0.9442 0.2017 0.3273 0.2472 0.6522 0.7366 0.2581
0.0446 5.0 1065 0.2691 0.6815 0.5847 0.6294 0.9486 0.2737 0.3415 0.3007 0.6703 0.7768 0.3243
0.0296 6.0 1278 0.2739 0.6740 0.5615 0.6126 0.9479 0.3065 0.3766 0.3333 0.7 0.7582 0.3472
0.0261 7.0 1491 0.3150 0.6907 0.5415 0.6071 0.9457 0.2292 0.3350 0.304 0.6369 0.7547 0.2982
0.0193 8.0 1704 0.2922 0.6957 0.5772 0.6310 0.9496 0.2887 0.3621 0.3676 0.7475 0.7645 0.4158
0.0173 9.0 1917 0.2823 0.6845 0.5963 0.6374 0.9501 0.25 0.3863 0.3660 0.6729 0.7810 0.4064
0.0227 10.0 2130 0.2912 0.6719 0.5681 0.6157 0.9482 0.2268 0.3797 0.3625 0.7045 0.7572 0.4286
0.0185 11.0 2343 0.3140 0.6941 0.5598 0.6198 0.9482 0.2532 0.3896 0.3382 0.7059 0.7601 0.3961
0.0221 12.0 2556 0.3527 0.6937 0.5473 0.6119 0.9470 0.3220 0.3687 0.35 0.7245 0.7502 0.3308
0.0099 13.0 2769 0.3332 0.6872 0.5748 0.6260 0.9493 0.3168 0.3782 0.3597 0.7391 0.7627 0.4027
0.0062 14.0 2982 0.3637 0.7287 0.5465 0.6246 0.9479 0.25 0.3700 0.4065 0.7340 0.7526 0.3468
0.0075 15.0 3195 0.3239 0.6913 0.5914 0.6374 0.9499 0.2703 0.3636 0.4030 0.7500 0.7733 0.4152

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.11.0+cu113
  • Datasets 2.0.0
  • Tokenizers 0.11.6