--- 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: 0.3960 - Precision: 0.4297 - Recall: 0.3910 - F1: 0.4094 - Accuracy: 0.8851 - Adr Precision: 0.3380 - Adr Recall: 0.3474 - Adr F1: 0.3427 - Disease Precision: 0.0 - Disease Recall: 0.0 - Disease F1: 0.0 - Drug Precision: 0.7857 - Drug Recall: 0.7606 - Drug F1: 0.7730 - 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: 0.6115 - B-adr Recall: 0.4189 - B-adr F1: 0.4972 - B-disease Precision: 0.0 - B-disease Recall: 0.0 - B-disease F1: 0.0 - B-drug Precision: 0.9605 - B-drug Recall: 0.7766 - B-drug F1: 0.8588 - 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: 0.2584 - I-adr Recall: 0.2621 - I-adr F1: 0.2602 - I-disease Precision: 0.0 - I-disease Recall: 0.0 - I-disease F1: 0.0 - I-drug Precision: 0.8362 - I-drug Recall: 0.7914 - I-drug F1: 0.8132 - 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: 0.2429 - Weighted Avg F1: 0.4447 ## 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 | 0.8678 | 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 | 0.6019 | 0.1340 | 0.0665 | 0.0889 | 0.8179 | 0.1340 | 0.0945 | 0.1108 | 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 | 0.0742 | 0.0646 | 0.0691 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0069 | 0.0221 | | No log | 3.0 | 48 | 0.5072 | 0.2736 | 0.1812 | 0.2180 | 0.8520 | 0.2615 | 0.1991 | 0.2261 | 0.0 | 0.0 | 0.0 | 0.3252 | 0.2128 | 0.2572 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4828 | 0.0220 | 0.0422 | 0.0 | 0.0 | 0.0 | 0.9837 | 0.6436 | 0.7781 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0870 | 0.0808 | 0.0838 | 0.0 | 0.0 | 0.0 | 0.9756 | 0.2139 | 0.3509 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1255 | 0.1639 | | No log | 4.0 | 64 | 0.4559 | 0.3937 | 0.3050 | 0.3437 | 0.8617 | 0.2798 | 0.2558 | 0.2673 | 0.0 | 0.0 | 0.0 | 0.9531 | 0.6489 | 0.7722 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4340 | 0.0362 | 0.0669 | 0.0 | 0.0 | 0.0 | 0.9843 | 0.6649 | 0.7937 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0882 | 0.0969 | 0.0924 | 0.0 | 0.0 | 0.0 | 0.9685 | 0.6578 | 0.7834 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1736 | 0.2238 | | No log | 5.0 | 80 | 0.4265 | 0.3860 | 0.3275 | 0.3544 | 0.8675 | 0.2885 | 0.2834 | 0.2859 | 0.0 | 0.0 | 0.0 | 0.8170 | 0.6649 | 0.7331 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5561 | 0.1638 | 0.2530 | 0.0 | 0.0 | 0.0 | 0.9845 | 0.6755 | 0.8013 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1221 | 0.1346 | 0.1281 | 0.0 | 0.0 | 0.0 | 0.8627 | 0.7059 | 0.7765 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1959 | 0.3032 | | No log | 6.0 | 96 | 0.4091 | 0.3964 | 0.3388 | 0.3653 | 0.8748 | 0.2965 | 0.2922 | 0.2943 | 0.0 | 0.0 | 0.0 | 0.8280 | 0.6915 | 0.7536 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5956 | 0.2551 | 0.3572 | 0.0 | 0.0 | 0.0 | 0.9852 | 0.7074 | 0.8235 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1712 | 0.1813 | 0.1761 | 0.0 | 0.0 | 0.0 | 0.8599 | 0.7219 | 0.7849 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2142 | 0.3599 | | No log | 7.0 | 112 | 0.4000 | 0.3940 | 0.3521 | 0.3719 | 0.8768 | 0.2999 | 0.3038 | 0.3018 | 0.0 | 0.0 | 0.0 | 0.7670 | 0.7181 | 0.7418 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6077 | 0.2976 | 0.3996 | 0.0 | 0.0 | 0.0 | 0.9718 | 0.7340 | 0.8364 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1853 | 0.1993 | 0.1920 | 0.0 | 0.0 | 0.0 | 0.8343 | 0.7807 | 0.8066 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2235 | 0.3841 | | No log | 8.0 | 128 | 0.4010 | 0.4306 | 0.3715 | 0.3989 | 0.8816 | 0.3363 | 0.3314 | 0.3338 | 0.0 | 0.0 | 0.0 | 0.8182 | 0.7181 | 0.7649 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.616 | 0.3638 | 0.4574 | 0.0 | 0.0 | 0.0 | 0.9789 | 0.7394 | 0.8424 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2316 | 0.2316 | 0.2316 | 0.0 | 0.0 | 0.0 | 0.8598 | 0.7540 | 0.8034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2335 | 0.4182 | | No log | 9.0 | 144 | 0.3963 | 0.4247 | 0.3869 | 0.4049 | 0.8839 | 0.3315 | 0.3430 | 0.3371 | 0.0 | 0.0 | 0.0 | 0.7978 | 0.7553 | 0.7760 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6047 | 0.4047 | 0.4849 | 0.0 | 0.0 | 0.0 | 0.9669 | 0.7766 | 0.8614 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2483 | 0.2549 | 0.2516 | 0.0 | 0.0 | 0.0 | 0.8497 | 0.7861 | 0.8167 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2414 | 0.4381 | | No log | 10.0 | 160 | 0.3960 | 0.4297 | 0.3910 | 0.4094 | 0.8851 | 0.3380 | 0.3474 | 0.3427 | 0.0 | 0.0 | 0.0 | 0.7857 | 0.7606 | 0.7730 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6115 | 0.4189 | 0.4972 | 0.0 | 0.0 | 0.0 | 0.9605 | 0.7766 | 0.8588 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2584 | 0.2621 | 0.2602 | 0.0 | 0.0 | 0.0 | 0.8362 | 0.7914 | 0.8132 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2429 | 0.4447 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0