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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: distilbert-base-uncased-finetuned-ner-cadec-active
    results: []

distilbert-base-uncased-finetuned-ner-cadec-active

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

  • Loss: 0.3503
  • Precision: 0.4853
  • Recall: 0.5067
  • F1: 0.4957
  • Accuracy: 0.9003
  • Adr Precision: 0.4156
  • Adr Recall: 0.4971
  • Adr F1: 0.4527
  • Disease Precision: 0.0
  • Disease Recall: 0.0
  • Disease F1: 0.0
  • Drug Precision: 0.7766
  • Drug Recall: 0.8138
  • Drug F1: 0.7948
  • Finding Precision: 0.0
  • Finding Recall: 0.0
  • Finding F1: 0.0
  • Symptom Precision: 0.0
  • Symptom Recall: 0.0
  • Symptom F1: 0.0
  • B-adr Precision: 0.6582
  • B-adr Recall: 0.6551
  • B-adr F1: 0.6567
  • B-disease Precision: 0.0
  • B-disease Recall: 0.0
  • B-disease F1: 0.0
  • B-drug Precision: 0.9639
  • B-drug Recall: 0.8511
  • B-drug F1: 0.9040
  • B-finding Precision: 0.0
  • B-finding Recall: 0.0
  • B-finding F1: 0.0
  • B-symptom Precision: 0.0
  • B-symptom Recall: 0.0
  • B-symptom F1: 0.0
  • I-adr Precision: 0.4091
  • I-adr Recall: 0.4686
  • I-adr F1: 0.4368
  • I-disease Precision: 0.0
  • I-disease Recall: 0.0
  • I-disease F1: 0.0
  • I-drug Precision: 0.8112
  • I-drug Recall: 0.8503
  • I-drug F1: 0.8303
  • I-finding Precision: 0.0
  • I-finding Recall: 0.0
  • I-finding F1: 0.0
  • I-symptom Precision: 0.0
  • I-symptom Recall: 0.0
  • I-symptom F1: 0.0
  • Macro Avg F1: 0.2828
  • Weighted Avg F1: 0.5661

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: 10

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy Adr Precision Adr Recall Adr F1 Disease Precision Disease Recall Disease F1 Drug Precision Drug Recall Drug F1 Finding Precision Finding Recall Finding F1 Symptom Precision Symptom Recall Symptom F1 B-adr Precision B-adr Recall B-adr F1 B-disease Precision B-disease Recall B-disease F1 B-drug Precision B-drug Recall B-drug F1 B-finding Precision B-finding Recall B-finding F1 B-symptom Precision B-symptom Recall B-symptom F1 I-adr Precision I-adr Recall I-adr F1 I-disease Precision I-disease Recall I-disease F1 I-drug Precision I-drug Recall I-drug F1 I-finding Precision I-finding Recall I-finding F1 I-symptom Precision I-symptom Recall I-symptom F1 Macro Avg F1 Weighted Avg F1
No log 1.0 24 0.7793 0.0 0.0 0.0 0.7876 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
No log 2.0 48 0.5127 0.3553 0.2375 0.2847 0.8510 0.2110 0.1613 0.1829 0.0 0.0 0.0 0.9528 0.6436 0.7683 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9841 0.6596 0.7898 0.0 0.0 0.0 0.0 0.0 0.0 0.0589 0.0557 0.0572 0.0 0.0 0.0 0.9603 0.6471 0.7732 0.0 0.0 0.0 0.0 0.0 0.0 0.1620 0.1866
No log 3.0 72 0.4218 0.4082 0.3275 0.3634 0.8659 0.3101 0.2849 0.2970 0.0 0.0 0.0 0.8158 0.6596 0.7294 0.0 0.0 0.0 0.0 0.0 0.0 0.6667 0.0378 0.0715 0.0 0.0 0.0 0.9577 0.7234 0.8242 0.0 0.0 0.0 0.0 0.0 0.0 0.1003 0.1131 0.1063 0.0 0.0 0.0 0.8658 0.6898 0.7679 0.0 0.0 0.0 0.0 0.0 0.0 0.1770 0.2316
No log 4.0 96 0.3889 0.4168 0.3767 0.3957 0.8763 0.3183 0.3183 0.3183 0.0 0.0 0.0 0.7641 0.7926 0.7781 0.0 0.0 0.0 0.0 0.0 0.0 0.6684 0.2031 0.3116 0.0 0.0 0.0 0.95 0.8085 0.8736 0.0 0.0 0.0 0.0 0.0 0.0 0.1453 0.1688 0.1561 0.0 0.0 0.0 0.7824 0.8075 0.7947 0.0 0.0 0.0 0.0 0.0 0.0 0.2136 0.3433
No log 5.0 120 0.3734 0.4409 0.3859 0.4116 0.8866 0.3450 0.3285 0.3366 0.0 0.0 0.0 0.755 0.8032 0.7784 0.0 0.0 0.0 0.0 0.0 0.0 0.6884 0.3165 0.4337 0.0 0.0 0.0 0.9691 0.8351 0.8971 0.0 0.0 0.0 0.0 0.0 0.0 0.2305 0.2442 0.2371 0.0 0.0 0.0 0.7588 0.8075 0.7824 0.0 0.0 0.0 0.0 0.0 0.0 0.2350 0.4149
No log 6.0 144 0.3626 0.4608 0.4156 0.4370 0.8903 0.3699 0.3721 0.3710 0.0 0.0 0.0 0.7937 0.7979 0.7958 0.0 0.0 0.0 0.0 0.0 0.0 0.6873 0.4016 0.5070 0.0 0.0 0.0 0.9689 0.8298 0.8940 0.0 0.0 0.0 0.0 0.0 0.0 0.2703 0.2926 0.2810 0.0 0.0 0.0 0.8032 0.8075 0.8053 0.0 0.0 0.0 0.0 0.0 0.0 0.2487 0.4579
No log 7.0 168 0.3535 0.4488 0.4759 0.4620 0.8947 0.3723 0.4491 0.4071 0.0 0.0 0.0 0.7573 0.8298 0.7919 0.0 0.0 0.0 0.0 0.0 0.0 0.6261 0.5827 0.6036 0.0 0.0 0.0 0.9422 0.8670 0.9030 0.0 0.0 0.0 0.0 0.0 0.0 0.3303 0.3860 0.3560 0.0 0.0 0.0 0.7843 0.8556 0.8184 0.0 0.0 0.0 0.0 0.0 0.0 0.2681 0.5195
No log 8.0 192 0.3528 0.4751 0.4985 0.4865 0.8969 0.4055 0.4898 0.4437 0.0 0.0 0.0 0.7732 0.7979 0.7853 0.0 0.0 0.0 0.0 0.0 0.0 0.6410 0.6299 0.6354 0.0 0.0 0.0 0.9636 0.8457 0.9008 0.0 0.0 0.0 0.0 0.0 0.0 0.3752 0.4399 0.4050 0.0 0.0 0.0 0.7979 0.8235 0.8105 0.0 0.0 0.0 0.0 0.0 0.0 0.2752 0.5457
No log 9.0 216 0.3483 0.4822 0.5005 0.4912 0.8984 0.4108 0.4884 0.4462 0.0 0.0 0.0 0.7806 0.8138 0.7969 0.0 0.0 0.0 0.0 0.0 0.0 0.6589 0.6205 0.6391 0.0 0.0 0.0 0.9639 0.8511 0.9040 0.0 0.0 0.0 0.0 0.0 0.0 0.3777 0.4434 0.4079 0.0 0.0 0.0 0.8103 0.8449 0.8272 0.0 0.0 0.0 0.0 0.0 0.0 0.2778 0.5501
No log 10.0 240 0.3503 0.4853 0.5067 0.4957 0.9003 0.4156 0.4971 0.4527 0.0 0.0 0.0 0.7766 0.8138 0.7948 0.0 0.0 0.0 0.0 0.0 0.0 0.6582 0.6551 0.6567 0.0 0.0 0.0 0.9639 0.8511 0.9040 0.0 0.0 0.0 0.0 0.0 0.0 0.4091 0.4686 0.4368 0.0 0.0 0.0 0.8112 0.8503 0.8303 0.0 0.0 0.0 0.0 0.0 0.0 0.2828 0.5661

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.15.0
  • Tokenizers 0.15.0