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BioMedical_NER-maccrobat-distilbert

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distilbert
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BioMedical_NER-maccrobat-distilbert / README.md
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metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: BioMedical_NER-maccrobat-distilbert
    results: []

BioMedical_NER-maccrobat-distilbert

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3622
  • Precision: 0.8525
  • Recall: 0.9225
  • F1: 0.8861
  • Accuracy: 0.9419

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: 50

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 45 1.7742 0.6341 0.0055 0.0109 0.6272
No log 2.0 90 1.4622 0.3671 0.1659 0.2286 0.6546
No log 3.0 135 1.2643 0.3409 0.2941 0.3158 0.6742
No log 4.0 180 1.1501 0.4039 0.4283 0.4157 0.7028
No log 5.0 225 1.0938 0.4145 0.5229 0.4624 0.7098
No log 6.0 270 1.0439 0.4371 0.5780 0.4978 0.7257
No log 7.0 315 0.9442 0.4997 0.6147 0.5513 0.7583
No log 8.0 360 0.9376 0.5126 0.6591 0.5767 0.7629
No log 9.0 405 0.8024 0.5512 0.6753 0.6070 0.7921
No log 10.0 450 0.7367 0.5949 0.6842 0.6364 0.8121
No log 11.0 495 0.7276 0.5959 0.7209 0.6525 0.8222
1.0374 12.0 540 0.6606 0.6329 0.7289 0.6775 0.8369
1.0374 13.0 585 0.6466 0.6335 0.7530 0.6881 0.8423
1.0374 14.0 630 0.6825 0.6200 0.7716 0.6875 0.8397
1.0374 15.0 675 0.5721 0.6767 0.7777 0.7237 0.8657
1.0374 16.0 720 0.5446 0.6965 0.7876 0.7393 0.8771
1.0374 17.0 765 0.5136 0.7475 0.7881 0.7672 0.8872
1.0374 18.0 810 0.5248 0.7185 0.8218 0.7667 0.8866
1.0374 19.0 855 0.4944 0.7494 0.8284 0.7869 0.8961
1.0374 20.0 900 0.5092 0.7299 0.8391 0.7807 0.8920
1.0374 21.0 945 0.4491 0.7775 0.8393 0.8072 0.9083
1.0374 22.0 990 0.4400 0.7744 0.8537 0.8121 0.9104
0.3072 23.0 1035 0.4593 0.7689 0.8619 0.8128 0.9091
0.3072 24.0 1080 0.4547 0.7726 0.8670 0.8171 0.9094
0.3072 25.0 1125 0.4425 0.7825 0.8689 0.8234 0.9141
0.3072 26.0 1170 0.4229 0.7949 0.8712 0.8313 0.9184
0.3072 27.0 1215 0.4015 0.8192 0.8731 0.8453 0.9241
0.3072 28.0 1260 0.4222 0.7995 0.8771 0.8365 0.9197
0.3072 29.0 1305 0.4119 0.8017 0.8849 0.8413 0.9217
0.3072 30.0 1350 0.3960 0.8217 0.8864 0.8528 0.9276
0.3072 31.0 1395 0.3965 0.8204 0.8919 0.8547 0.9278
0.3072 32.0 1440 0.3936 0.8222 0.8972 0.8581 0.9282
0.3072 33.0 1485 0.3979 0.8263 0.8991 0.8612 0.9299
0.1369 34.0 1530 0.3799 0.8352 0.8989 0.8659 0.9333
0.1369 35.0 1575 0.3712 0.8407 0.9054 0.8718 0.9356
0.1369 36.0 1620 0.3648 0.8443 0.9046 0.8734 0.9368
0.1369 37.0 1665 0.3640 0.8414 0.9048 0.8719 0.9368
0.1369 38.0 1710 0.3632 0.8473 0.9088 0.8770 0.9385
0.1369 39.0 1755 0.3765 0.8369 0.9118 0.8727 0.9363
0.1369 40.0 1800 0.3686 0.8465 0.9107 0.8775 0.9382
0.1369 41.0 1845 0.3644 0.8461 0.9158 0.8796 0.9389
0.1369 42.0 1890 0.3676 0.8446 0.9156 0.8786 0.9390
0.1369 43.0 1935 0.3667 0.8451 0.9177 0.8799 0.9397
0.1369 44.0 1980 0.3622 0.8502 0.9189 0.8832 0.9407
0.0844 45.0 2025 0.3628 0.8535 0.9187 0.8849 0.9410
0.0844 46.0 2070 0.3677 0.8510 0.9198 0.8840 0.9406
0.0844 47.0 2115 0.3670 0.8521 0.9229 0.8861 0.9410
0.0844 48.0 2160 0.3627 0.8532 0.9227 0.8866 0.9417
0.0844 49.0 2205 0.3640 0.8511 0.9232 0.8857 0.9417
0.0844 50.0 2250 0.3622 0.8525 0.9225 0.8861 0.9419

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3