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bertweet-base_wnut17_ner

Token Classification
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PyTorch
Safetensors
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_LP_FP
    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: 0.3376
  • Overall Precision: 0.6803
  • Overall Recall: 0.6096
  • Overall F1: 0.6430
  • Overall Accuracy: 0.9509
  • Corporation F1: 0.2975
  • Creative-work F1: 0.4436
  • Group F1: 0.3624
  • Location F1: 0.6834
  • Person F1: 0.7902
  • Product F1: 0.3887

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: 1e-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
0.0215 1.0 213 0.2913 0.7026 0.5905 0.6417 0.9507 0.2832 0.4444 0.2975 0.6854 0.7788 0.4015
0.0213 2.0 426 0.3052 0.6774 0.5772 0.6233 0.9495 0.2830 0.3483 0.3231 0.6857 0.7728 0.3794
0.0288 3.0 639 0.3378 0.7061 0.5507 0.6188 0.9467 0.3077 0.4184 0.3529 0.6222 0.7532 0.3910
0.0124 4.0 852 0.2712 0.6574 0.6121 0.6340 0.9502 0.3077 0.4842 0.3167 0.6809 0.7735 0.3986
0.0208 5.0 1065 0.2905 0.7108 0.6063 0.6544 0.9518 0.3063 0.4286 0.3419 0.7052 0.7913 0.4223
0.0071 6.0 1278 0.3189 0.6756 0.5847 0.6269 0.9494 0.2759 0.4380 0.3256 0.6744 0.7781 0.3779
0.0073 7.0 1491 0.3593 0.7330 0.5540 0.6310 0.9476 0.3061 0.4388 0.3784 0.6946 0.7631 0.3374
0.0135 8.0 1704 0.3564 0.6875 0.5482 0.6100 0.9471 0.34 0.4179 0.3088 0.6632 0.7486 0.3695
0.0097 9.0 1917 0.3085 0.6598 0.6395 0.6495 0.9516 0.3111 0.4609 0.3836 0.7090 0.7906 0.4083
0.0108 10.0 2130 0.3045 0.6605 0.6478 0.6541 0.9509 0.3529 0.4580 0.3649 0.6897 0.7843 0.4387
0.013 11.0 2343 0.3383 0.6788 0.6179 0.6470 0.9507 0.2783 0.4248 0.3358 0.7368 0.7958 0.3655
0.0076 12.0 2556 0.3617 0.6920 0.5523 0.6143 0.9474 0.2708 0.3985 0.3333 0.6740 0.7566 0.3525
0.0042 13.0 2769 0.3747 0.6896 0.5664 0.6220 0.9473 0.2478 0.3915 0.3521 0.6561 0.7742 0.3539
0.0049 14.0 2982 0.3376 0.6803 0.6096 0.6430 0.9509 0.2975 0.4436 0.3624 0.6834 0.7902 0.3887

Framework versions

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