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ner-fine-tune-roberta-new

Token Classification
Transformers
PyTorch
roberta
Generated from Trainer
Inference Endpoints
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metadata
license: mit
base_model: roberta-base
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ner-fine-tune-roberta-new
    results: []

ner-fine-tune-roberta-new

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

  • Loss: 0.3320
  • Precision: 0.2696
  • Recall: 0.3767
  • F1: 0.3143
  • Accuracy: 0.9389

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: 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: 30

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 122 0.2264 0.0 0.0 0.0 0.9542
No log 2.0 244 0.1874 0.2348 0.1349 0.1713 0.9571
No log 3.0 366 0.1739 0.2420 0.2279 0.2347 0.9528
No log 4.0 488 0.1656 0.1939 0.2233 0.2076 0.9472
0.2548 5.0 610 0.1740 0.3243 0.2767 0.2986 0.9573
0.2548 6.0 732 0.2087 0.2562 0.2628 0.2595 0.9464
0.2548 7.0 854 0.1921 0.2773 0.2953 0.2860 0.9495
0.2548 8.0 976 0.2038 0.2602 0.3860 0.3109 0.9397
0.0748 9.0 1098 0.2324 0.2371 0.3093 0.2684 0.9398
0.0748 10.0 1220 0.2329 0.2852 0.3442 0.3119 0.9436
0.0748 11.0 1342 0.2670 0.2521 0.3535 0.2943 0.9356
0.0748 12.0 1464 0.2607 0.2509 0.3186 0.2807 0.9395
0.033 13.0 1586 0.2645 0.2655 0.3791 0.3123 0.9359
0.033 14.0 1708 0.2947 0.2838 0.4442 0.3463 0.9398
0.033 15.0 1830 0.2807 0.2945 0.3349 0.3134 0.9451
0.033 16.0 1952 0.2990 0.2910 0.3302 0.3094 0.9448
0.0181 17.0 2074 0.2915 0.2799 0.3651 0.3169 0.9425
0.0181 18.0 2196 0.2853 0.2868 0.3535 0.3167 0.9424
0.0181 19.0 2318 0.2991 0.2918 0.3814 0.3306 0.9440
0.0181 20.0 2440 0.2863 0.2762 0.3744 0.3179 0.9408
0.0111 21.0 2562 0.3280 0.2796 0.3628 0.3158 0.9409
0.0111 22.0 2684 0.3135 0.2772 0.3372 0.3043 0.9431
0.0111 23.0 2806 0.3282 0.2632 0.3698 0.3075 0.9404
0.0111 24.0 2928 0.3306 0.2597 0.3884 0.3113 0.9369
0.0078 25.0 3050 0.3135 0.2743 0.3605 0.3116 0.9414
0.0078 26.0 3172 0.3320 0.2646 0.3791 0.3117 0.9374
0.0078 27.0 3294 0.3273 0.2659 0.3791 0.3126 0.9381
0.0078 28.0 3416 0.3290 0.2616 0.3674 0.3056 0.9380
0.0055 29.0 3538 0.3366 0.2656 0.3860 0.3147 0.9372
0.0055 30.0 3660 0.3320 0.2696 0.3767 0.3143 0.9389

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1