Hugging Face's logo

mireiaplalis
/
bert-base-cased-finetuned-ner-cadec

Token Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
Edit model card

bert-base-cased-finetuned-ner-cadec

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

  • Loss: 0.3476
  • Precision: 0.5870
  • Recall: 0.6866
  • F1: 0.6329
  • Accuracy: 0.9193
  • Adr Precision: 0.5614
  • Adr Recall: 0.6881
  • Adr F1: 0.6183
  • Disease Precision: 0.0
  • Disease Recall: 0.0
  • Disease F1: 0.0
  • Drug Precision: 0.8988
  • Drug Recall: 0.9152
  • Drug F1: 0.9069
  • Finding Precision: 0.2295
  • Finding Recall: 0.3111
  • Finding F1: 0.2642
  • Symptom Precision: 0.4762
  • Symptom Recall: 0.3704
  • Symptom F1: 0.4167
  • B-adr Precision: 0.7133
  • B-adr Recall: 0.8119
  • B-adr F1: 0.7594
  • B-disease Precision: 0.0
  • B-disease Recall: 0.0
  • B-disease F1: 0.0
  • B-drug Precision: 0.9639
  • B-drug Recall: 0.9697
  • B-drug F1: 0.9668
  • B-finding Precision: 0.3469
  • B-finding Recall: 0.3778
  • B-finding F1: 0.3617
  • B-symptom Precision: 0.7857
  • B-symptom Recall: 0.44
  • B-symptom F1: 0.5641
  • I-adr Precision: 0.5799
  • I-adr Recall: 0.6991
  • I-adr F1: 0.6340
  • I-disease Precision: 0.0
  • I-disease Recall: 0.0
  • I-disease F1: 0.0
  • I-drug Precision: 0.9042
  • I-drug Recall: 0.9152
  • I-drug F1: 0.9096
  • I-finding Precision: 0.2979
  • I-finding Recall: 0.3684
  • I-finding F1: 0.3294
  • I-symptom Precision: 0.3333
  • I-symptom Recall: 0.2
  • I-symptom F1: 0.25
  • Macro Avg F1: 0.4775
  • Weighted Avg F1: 0.7087

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 1.0 127 0.2830 0.4796 0.6005 0.5333 0.9082 0.4248 0.6220 0.5048 0.0 0.0 0.0 0.7966 0.8545 0.8246 0.1 0.0222 0.0364 0.0 0.0 0.0 0.6122 0.7908 0.6901 0.0 0.0 0.0 0.9157 0.9212 0.9184 0.5714 0.0889 0.1538 0.0 0.0 0.0 0.4687 0.6472 0.5436 0.0556 0.0625 0.0588 0.8161 0.8606 0.8378 0.2857 0.0526 0.0889 0.0 0.0 0.0 0.3291 0.6177
No log 2.0 254 0.2472 0.5073 0.6092 0.5536 0.9125 0.4913 0.6183 0.5475 0.0227 0.0526 0.0317 0.8571 0.8727 0.8649 0.0984 0.1333 0.1132 0.0 0.0 0.0 0.7092 0.7582 0.7328 0.3333 0.0526 0.0909 0.9568 0.9394 0.9480 0.3542 0.3778 0.3656 0.0 0.0 0.0 0.5275 0.6429 0.5795 0.0714 0.1875 0.1034 0.8788 0.8788 0.8788 0.1667 0.1316 0.1471 0.0 0.0 0.0 0.3846 0.6615
No log 3.0 381 0.2629 0.5733 0.6542 0.6111 0.9177 0.5495 0.6624 0.6007 0.075 0.1579 0.1017 0.8982 0.9091 0.9036 0.125 0.1111 0.1176 0.5 0.1852 0.2703 0.7105 0.7774 0.7424 0.2174 0.2632 0.2381 0.9578 0.9636 0.9607 0.2963 0.1778 0.2222 0.5 0.2 0.2857 0.5783 0.6797 0.6249 0.0882 0.1875 0.12 0.9146 0.9091 0.9119 0.2609 0.1579 0.1967 0.0 0.0 0.0 0.4303 0.6880
0.2709 4.0 508 0.2630 0.5877 0.6567 0.6203 0.9177 0.5499 0.6569 0.5987 0.0 0.0 0.0 0.8922 0.9030 0.8976 0.2459 0.3333 0.2830 0.5 0.1481 0.2286 0.7219 0.7774 0.7486 0.0 0.0 0.0 0.9518 0.9576 0.9547 0.3061 0.3333 0.3191 0.5 0.16 0.2424 0.5759 0.6818 0.6244 0.0 0.0 0.0 0.9146 0.9091 0.9119 0.3333 0.4737 0.3913 0.0 0.0 0.0 0.4192 0.6923
0.2709 5.0 635 0.2856 0.5714 0.6542 0.6100 0.9180 0.5455 0.6606 0.5975 0.075 0.1579 0.1017 0.9085 0.9030 0.9058 0.1667 0.1333 0.1481 0.3529 0.2222 0.2727 0.7284 0.7774 0.7521 0.1429 0.2105 0.1702 0.9693 0.9576 0.9634 0.2917 0.1556 0.2029 0.5 0.24 0.3243 0.5616 0.6905 0.6194 0.1176 0.25 0.1600 0.9202 0.9091 0.9146 0.25 0.1579 0.1935 0.5 0.15 0.2308 0.4531 0.6930
0.2709 6.0 762 0.3053 0.5488 0.6529 0.5964 0.9140 0.5331 0.6642 0.5915 0.0 0.0 0.0 0.8976 0.9030 0.9003 0.0962 0.1111 0.1031 0.4667 0.2593 0.3333 0.7073 0.8023 0.7518 0.0 0.0 0.0 0.9636 0.9636 0.9636 0.2927 0.2667 0.2791 0.7273 0.32 0.4444 0.5554 0.6732 0.6086 0.1053 0.25 0.1481 0.9030 0.9030 0.9030 0.2222 0.1579 0.1846 0.6 0.15 0.24 0.4523 0.6902
0.2709 7.0 889 0.3162 0.5816 0.6717 0.6234 0.9200 0.5605 0.6716 0.6110 0.0 0.0 0.0 0.9102 0.9212 0.9157 0.1607 0.2 0.1782 0.5 0.4074 0.4490 0.7207 0.8023 0.7593 0.1667 0.0526 0.08 0.9639 0.9697 0.9668 0.3261 0.3333 0.3297 0.6875 0.44 0.5366 0.5769 0.6818 0.6250 0.0385 0.0625 0.0476 0.9268 0.9212 0.9240 0.2 0.2105 0.2051 0.4545 0.25 0.3226 0.4797 0.7054
0.0894 8.0 1016 0.3347 0.5935 0.6891 0.6378 0.9181 0.5595 0.6899 0.6179 0.0 0.0 0.0 0.8876 0.9091 0.8982 0.2712 0.3556 0.3077 0.5556 0.3704 0.4444 0.7167 0.8157 0.7630 0.0 0.0 0.0 0.9581 0.9697 0.9639 0.3404 0.3556 0.3478 0.8462 0.44 0.5789 0.5786 0.7013 0.6341 0.0 0.0 0.0 0.8929 0.9091 0.9009 0.3265 0.4211 0.3678 0.4444 0.2 0.2759 0.4832 0.7099
0.0894 9.0 1143 0.3441 0.5813 0.6742 0.6243 0.9194 0.5549 0.6771 0.6099 0.0 0.0 0.0 0.8817 0.9030 0.8922 0.2182 0.2667 0.2400 0.5263 0.3704 0.4348 0.7197 0.8081 0.7613 0.0 0.0 0.0 0.9524 0.9697 0.9610 0.3478 0.3556 0.3516 0.8462 0.44 0.5789 0.5727 0.6905 0.6261 0.0 0.0 0.0 0.8976 0.9030 0.9003 0.2683 0.2895 0.2785 0.4 0.2 0.2667 0.4724 0.7041
0.0894 10.0 1270 0.3476 0.5870 0.6866 0.6329 0.9193 0.5614 0.6881 0.6183 0.0 0.0 0.0 0.8988 0.9152 0.9069 0.2295 0.3111 0.2642 0.4762 0.3704 0.4167 0.7133 0.8119 0.7594 0.0 0.0 0.0 0.9639 0.9697 0.9668 0.3469 0.3778 0.3617 0.7857 0.44 0.5641 0.5799 0.6991 0.6340 0.0 0.0 0.0 0.9042 0.9152 0.9096 0.2979 0.3684 0.3294 0.3333 0.2 0.25 0.4775 0.7087

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0
Downloads last month
99
Safetensors
Model size
108M params
Tensor type
F32
·
Inference Examples
Token Classification
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for mireiaplalis/bert-base-cased-finetuned-ner-cadec

Base model

google-bert/bert-base-cased
Finetuned
(1851)
this model

Evaluation results

Metadata error: specify a dataset to view leaderboard