Hugging Face's logo

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

Token Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
distilbert-base-uncased-finetuned-ner-cadec-active / README.md
csNoHug's picture
csNoHug
Training complete
2c6e992
preview code
|
raw
history blame
12.9 kB
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: 0.3797
  • Precision: 0.4683
  • Recall: 0.4688
  • F1: 0.4685
  • Accuracy: 0.8922
  • Adr Precision: 0.3881
  • Adr Recall: 0.4462
  • Adr F1: 0.4151
  • Disease Precision: 0.0
  • Disease Recall: 0.0
  • Disease F1: 0.0
  • Drug Precision: 0.8075
  • Drug Recall: 0.8032
  • Drug F1: 0.8053
  • 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: 0.6058
  • B-adr Recall: 0.5591
  • B-adr F1: 0.5815
  • B-disease Precision: 0.0
  • B-disease Recall: 0.0
  • B-disease F1: 0.0
  • B-drug Precision: 0.9571
  • B-drug Recall: 0.8298
  • B-drug F1: 0.8889
  • 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: 0.3421
  • I-adr Recall: 0.3734
  • I-adr F1: 0.3571
  • I-disease Precision: 0.0
  • I-disease Recall: 0.0
  • I-disease F1: 0.0
  • I-drug Precision: 0.8370
  • I-drug Recall: 0.8235
  • I-drug F1: 0.8302
  • 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: 0.2658
  • Weighted Avg F1: 0.5115

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 19 0.8069 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 38 0.5346 0.3171 0.1668 0.2186 0.8475 0.2268 0.1279 0.1636 0.0 0.0 0.0 0.5952 0.3989 0.4777 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 0.0747 0.0521 0.0614 0.0 0.0 0.0 0.9615 0.4011 0.5660 0.0 0.0 0.0 0.0 0.0 0.0 0.1417 0.1657
No log 3.0 57 0.4568 0.4028 0.2907 0.3377 0.8619 0.2777 0.2297 0.2514 0.0 0.0 0.0 0.9265 0.6702 0.7778 0.0 0.0 0.0 0.0 0.0 0.0 0.5124 0.0976 0.1640 0.0 0.0 0.0 0.9846 0.6809 0.8050 0.0 0.0 0.0 0.0 0.0 0.0 0.1075 0.1023 0.1049 0.0 0.0 0.0 0.9338 0.6791 0.7864 0.0 0.0 0.0 0.0 0.0 0.0 0.1860 0.2648
No log 4.0 76 0.4164 0.4110 0.3286 0.3652 0.8643 0.3018 0.2733 0.2868 0.0 0.0 0.0 0.8418 0.7074 0.7688 0.0 0.0 0.0 0.0 0.0 0.0 0.6184 0.0740 0.1322 0.0 0.0 0.0 0.9662 0.7606 0.8512 0.0 0.0 0.0 0.0 0.0 0.0 0.0855 0.0934 0.0893 0.0 0.0 0.0 0.8590 0.7166 0.7813 0.0 0.0 0.0 0.0 0.0 0.0 0.1854 0.2526
No log 5.0 95 0.3960 0.4371 0.3838 0.4087 0.8776 0.3417 0.3358 0.3387 0.0 0.0 0.0 0.7912 0.7660 0.7784 0.0 0.0 0.0 0.0 0.0 0.0 0.6197 0.2772 0.3830 0.0 0.0 0.0 0.9490 0.7926 0.8638 0.0 0.0 0.0 0.0 0.0 0.0 0.1893 0.2029 0.1958 0.0 0.0 0.0 0.8483 0.8075 0.8274 0.0 0.0 0.0 0.0 0.0 0.0 0.2270 0.3845
No log 6.0 114 0.3851 0.4465 0.4145 0.4299 0.8859 0.3537 0.3706 0.3620 0.0 0.0 0.0 0.8065 0.7979 0.8021 0.0 0.0 0.0 0.0 0.0 0.0 0.6179 0.3921 0.4798 0.0 0.0 0.0 0.9565 0.8191 0.8825 0.0 0.0 0.0 0.0 0.0 0.0 0.2441 0.2621 0.2528 0.0 0.0 0.0 0.8352 0.8128 0.8238 0.0 0.0 0.0 0.0 0.0 0.0 0.2439 0.4397
No log 7.0 133 0.3813 0.4567 0.4483 0.4525 0.8896 0.3697 0.4186 0.3926 0.0 0.0 0.0 0.8333 0.7979 0.8152 0.0 0.0 0.0 0.0 0.0 0.0 0.5981 0.5087 0.5498 0.0 0.0 0.0 0.9747 0.8191 0.8902 0.0 0.0 0.0 0.0 0.0 0.0 0.3088 0.3321 0.3201 0.0 0.0 0.0 0.8436 0.8075 0.8251 0.0 0.0 0.0 0.0 0.0 0.0 0.2585 0.4877
No log 8.0 152 0.3785 0.4644 0.4534 0.4588 0.8908 0.3817 0.4244 0.4019 0.0 0.0 0.0 0.7989 0.8032 0.8011 0.0 0.0 0.0 0.0 0.0 0.0 0.6143 0.4992 0.5508 0.0 0.0 0.0 0.9512 0.8298 0.8864 0.0 0.0 0.0 0.0 0.0 0.0 0.3245 0.3537 0.3385 0.0 0.0 0.0 0.8432 0.8342 0.8387 0.0 0.0 0.0 0.0 0.0 0.0 0.2614 0.4950
No log 9.0 171 0.3795 0.4542 0.4565 0.4553 0.8890 0.3711 0.4288 0.3978 0.0 0.0 0.0 0.8075 0.8032 0.8053 0.0 0.0 0.0 0.0 0.0 0.0 0.6049 0.5496 0.5759 0.0 0.0 0.0 0.9573 0.8351 0.8920 0.0 0.0 0.0 0.0 0.0 0.0 0.3236 0.3573 0.3396 0.0 0.0 0.0 0.8432 0.8342 0.8387 0.0 0.0 0.0 0.0 0.0 0.0 0.2646 0.5051
No log 10.0 190 0.3797 0.4683 0.4688 0.4685 0.8922 0.3881 0.4462 0.4151 0.0 0.0 0.0 0.8075 0.8032 0.8053 0.0 0.0 0.0 0.0 0.0 0.0 0.6058 0.5591 0.5815 0.0 0.0 0.0 0.9571 0.8298 0.8889 0.0 0.0 0.0 0.0 0.0 0.0 0.3421 0.3734 0.3571 0.0 0.0 0.0 0.8370 0.8235 0.8302 0.0 0.0 0.0 0.0 0.0 0.0 0.2658 0.5115

Framework versions

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