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bertweet-base_wnut17_ner

Token Classification
Transformers
PyTorch
Safetensors
roberta
Generated from Trainer
named-entity-recognition
Eval Results
Inference Endpoints
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bertweet-base_wnut17_ner / README.md
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WNUT 17 training
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - wnut_17
model-index:
  - name: fine_tune_bert_output
    results: []

fine_tune_bert_output

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: 0.3504
  • Overall Precision: 0.6850
  • Overall Recall: 0.6196
  • Overall F1: 0.6507
  • Overall Accuracy: 0.9502
  • Corporation F1: 0.2626
  • Creative-work F1: 0.4460
  • Group F1: 0.3692
  • Location F1: 0.7283
  • Person F1: 0.7928
  • Product F1: 0.3852

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
0.2954 1.0 213 0.4357 0.0 0.0 0.0 0.8979 0.0 0.0 0.0 0.0 0.0 0.0
0.1654 2.0 426 0.3435 0.5890 0.3929 0.4714 0.9307 0.0 0.0 0.0 0.4444 0.6181 0.0
0.1213 3.0 639 0.2981 0.6425 0.4776 0.5479 0.9380 0.1961 0.0565 0.1720 0.5507 0.7285 0.0
0.051 4.0 852 0.2958 0.7020 0.4892 0.5766 0.9430 0.2540 0.3458 0.2157 0.63 0.73 0.2388
0.0503 5.0 1065 0.3154 0.6659 0.5033 0.5733 0.9429 0.2373 0.3365 0.3212 0.6 0.7223 0.2896
0.026 6.0 1278 0.2787 0.6768 0.5706 0.6192 0.9479 0.256 0.4163 0.3699 0.7107 0.7717 0.3052
0.0271 7.0 1491 0.2940 0.7122 0.5714 0.6341 0.9486 0.25 0.3982 0.3676 0.7033 0.7717 0.3460
0.0239 8.0 1704 0.2872 0.6210 0.5839 0.6019 0.9461 0.2722 0.4188 0.2902 0.7389 0.7593 0.3597
0.0155 9.0 1917 0.2910 0.6863 0.5905 0.6348 0.9494 0.2623 0.4959 0.3433 0.6878 0.7708 0.3614
0.0122 10.0 2130 0.3067 0.6966 0.5797 0.6328 0.9490 0.2558 0.4609 0.3309 0.6842 0.7645 0.3835
0.0161 11.0 2343 0.2782 0.6637 0.6096 0.6355 0.9502 0.3103 0.4710 0.4275 0.6811 0.7688 0.4110
0.0232 12.0 2556 0.3123 0.6832 0.5822 0.6287 0.9495 0.3235 0.4722 0.4 0.7374 0.7568 0.4321
0.012 13.0 2769 0.3161 0.6663 0.5573 0.6070 0.9475 0.2128 0.4474 0.3289 0.7243 0.7400 0.4130
0.0051 14.0 2982 0.3241 0.7131 0.5864 0.6436 0.9498 0.3125 0.4839 0.3934 0.6952 0.7700 0.3802
0.0096 15.0 3195 0.3140 0.6924 0.6321 0.6609 0.9520 0.3036 0.4542 0.3576 0.7312 0.7953 0.432
0.0045 16.0 3408 0.3356 0.6917 0.6038 0.6448 0.9498 0.2899 0.4858 0.3582 0.6952 0.7840 0.4275
0.0066 17.0 3621 0.3308 0.6738 0.6262 0.6492 0.9510 0.2957 0.4333 0.3673 0.6989 0.7954 0.3320
0.0068 18.0 3834 0.3527 0.7157 0.6063 0.6565 0.9505 0.2353 0.4211 0.4058 0.7182 0.7991 0.3825
0.0033 19.0 4047 0.3522 0.7298 0.6013 0.6594 0.9514 0.2093 0.4454 0.4390 0.7006 0.7912 0.4000
0.0067 20.0 4260 0.3721 0.6859 0.5839 0.6308 0.9476 0.2774 0.3442 0.3881 0.7254 0.7844 0.3140
0.0083 21.0 4473 0.3504 0.6850 0.6196 0.6507 0.9502 0.2626 0.4460 0.3692 0.7283 0.7928 0.3852

Framework versions

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