--- 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: 0.3797 - 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