--- 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](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3873 - Precision: 0.4488 - Recall: 0.4483 - F1: 0.4485 - Accuracy: 0.8907 - Adr Precision: 0.3791 - Adr Recall: 0.4375 - Adr F1: 0.4062 - Disease Precision: 0.0 - Disease Recall: 0.0 - Disease F1: 0.0 - Drug Precision: 0.7527 - Drug Recall: 0.7287 - Drug F1: 0.7405 - 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: 0.6329 - B-adr Recall: 0.5512 - B-adr F1: 0.5892 - B-disease Precision: 0.0 - B-disease Recall: 0.0 - B-disease F1: 0.0 - B-drug Precision: 0.9718 - B-drug Recall: 0.7340 - B-drug F1: 0.8364 - 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: 0.3287 - I-adr Recall: 0.3860 - I-adr F1: 0.3551 - I-disease Precision: 0.0 - I-disease Recall: 0.0 - I-disease F1: 0.0 - I-drug Precision: 0.8066 - I-drug Recall: 0.7807 - I-drug F1: 0.7935 - 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: 0.2574 - Weighted Avg F1: 0.5041 ## 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 | 16 | 0.8554 | 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 | 32 | 0.6110 | 0.1709 | 0.0901 | 0.1180 | 0.8226 | 0.1709 | 0.1279 | 0.1463 | 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 | 0.0699 | 0.0646 | 0.0672 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0067 | 0.0215 | | No log | 3.0 | 48 | 0.5114 | 0.2118 | 0.1433 | 0.1709 | 0.8496 | 0.2612 | 0.2035 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.55 | 0.0173 | 0.0336 | 0.0 | 0.0 | 0.0 | 0.984 | 0.6543 | 0.7859 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0918 | 0.0880 | 0.0898 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0909 | 0.1259 | | No log | 4.0 | 64 | 0.4618 | 0.4412 | 0.3224 | 0.3726 | 0.8660 | 0.3271 | 0.2791 | 0.3012 | 0.0 | 0.0 | 0.0 | 0.9685 | 0.6543 | 0.7810 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6375 | 0.0803 | 0.1427 | 0.0 | 0.0 | 0.0 | 0.9843 | 0.6649 | 0.7937 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1209 | 0.1257 | 0.1232 | 0.0 | 0.0 | 0.0 | 0.9685 | 0.6578 | 0.7834 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1843 | 0.2613 | | No log | 5.0 | 80 | 0.4254 | 0.4072 | 0.3460 | 0.3741 | 0.8679 | 0.3080 | 0.3125 | 0.3102 | 0.0 | 0.0 | 0.0 | 0.9318 | 0.6543 | 0.7688 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5960 | 0.1858 | 0.2833 | 0.0 | 0.0 | 0.0 | 0.9843 | 0.6649 | 0.7937 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1381 | 0.1652 | 0.1504 | 0.0 | 0.0 | 0.0 | 0.9394 | 0.6631 | 0.7774 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2005 | 0.3207 | | No log | 6.0 | 96 | 0.4048 | 0.4377 | 0.4063 | 0.4214 | 0.8835 | 0.3634 | 0.3983 | 0.3800 | 0.0 | 0.0 | 0.0 | 0.8039 | 0.6543 | 0.7214 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6335 | 0.4409 | 0.5200 | 0.0 | 0.0 | 0.0 | 0.9766 | 0.6649 | 0.7911 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2772 | 0.3250 | 0.2992 | 0.0 | 0.0 | 0.0 | 0.8618 | 0.7005 | 0.7729 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2383 | 0.4538 | | No log | 7.0 | 112 | 0.3952 | 0.4114 | 0.3920 | 0.4015 | 0.8815 | 0.3303 | 0.3663 | 0.3473 | 0.0 | 0.0 | 0.0 | 0.7798 | 0.6968 | 0.7360 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6121 | 0.4126 | 0.4929 | 0.0 | 0.0 | 0.0 | 0.9784 | 0.7234 | 0.8318 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2394 | 0.2926 | 0.2633 | 0.0 | 0.0 | 0.0 | 0.8383 | 0.7487 | 0.7910 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2379 | 0.4388 | | No log | 8.0 | 128 | 0.3922 | 0.4575 | 0.4411 | 0.4492 | 0.8884 | 0.3821 | 0.4331 | 0.4060 | 0.0 | 0.0 | 0.0 | 0.8210 | 0.7074 | 0.76 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6331 | 0.5354 | 0.5802 | 0.0 | 0.0 | 0.0 | 0.9784 | 0.7234 | 0.8318 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3281 | 0.3788 | 0.3517 | 0.0 | 0.0 | 0.0 | 0.8758 | 0.7540 | 0.8103 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2574 | 0.5010 | | No log | 9.0 | 144 | 0.3886 | 0.4549 | 0.4391 | 0.4469 | 0.8887 | 0.3815 | 0.4259 | 0.4025 | 0.0 | 0.0 | 0.0 | 0.7771 | 0.7234 | 0.7493 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6271 | 0.5244 | 0.5712 | 0.0 | 0.0 | 0.0 | 0.9716 | 0.7287 | 0.8328 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3297 | 0.3770 | 0.3518 | 0.0 | 0.0 | 0.0 | 0.8333 | 0.7754 | 0.8033 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2559 | 0.4971 | | No log | 10.0 | 160 | 0.3873 | 0.4488 | 0.4483 | 0.4485 | 0.8907 | 0.3791 | 0.4375 | 0.4062 | 0.0 | 0.0 | 0.0 | 0.7527 | 0.7287 | 0.7405 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6329 | 0.5512 | 0.5892 | 0.0 | 0.0 | 0.0 | 0.9718 | 0.7340 | 0.8364 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3287 | 0.3860 | 0.3551 | 0.0 | 0.0 | 0.0 | 0.8066 | 0.7807 | 0.7935 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2574 | 0.5041 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0