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---
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: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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: 0.3989
- Precision: 0.4284
- Recall: 0.4258
- F1: 0.4271
- Accuracy: 0.8867
- Adr Precision: 0.3490
- Adr Recall: 0.3997
- Adr F1: 0.3726
- Disease Precision: 0.0
- Disease Recall: 0.0
- Disease F1: 0.0
- Drug Precision: 0.7705
- Drug Recall: 0.75
- Drug F1: 0.7601
- 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: 0.6208
- B-adr Recall: 0.5543
- B-adr F1: 0.5857
- B-disease Precision: 0.0
- B-disease Recall: 0.0
- B-disease F1: 0.0
- B-drug Precision: 0.9545
- B-drug Recall: 0.7819
- B-drug F1: 0.8596
- 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: 0.3112
- I-adr Recall: 0.3627
- I-adr F1: 0.3350
- I-disease Precision: 0.0
- I-disease Recall: 0.0
- I-disease F1: 0.0
- I-drug Precision: 0.8077
- I-drug Recall: 0.7861
- I-drug F1: 0.7967
- 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: 0.2577
- Weighted Avg F1: 0.4992
## 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 | 0.8682 | 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 | 0.6041 | 0.1482 | 0.0645 | 0.0899 | 0.8183 | 0.1482 | 0.0916 | 0.1132 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1153 | 0.0880 | 0.0998 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0100 | 0.0319 |
| No log | 3.0 | 48 | 0.5134 | 0.1863 | 0.1310 | 0.1538 | 0.8487 | 0.2282 | 0.1860 | 0.2050 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6154 | 0.0126 | 0.0247 | 0.0 | 0.0 | 0.0 | 0.9841 | 0.6596 | 0.7898 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0964 | 0.0969 | 0.0967 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0911 | 0.1252 |
| No log | 4.0 | 64 | 0.4579 | 0.3852 | 0.2938 | 0.3333 | 0.8634 | 0.2667 | 0.2384 | 0.2517 | 0.0 | 0.0 | 0.0 | 0.9462 | 0.6543 | 0.7736 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6322 | 0.0866 | 0.1524 | 0.0 | 0.0 | 0.0 | 0.9690 | 0.6649 | 0.7886 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1283 | 0.1400 | 0.1339 | 0.0 | 0.0 | 0.0 | 0.9688 | 0.6631 | 0.7873 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1862 | 0.2681 |
| No log | 5.0 | 80 | 0.4296 | 0.3784 | 0.3265 | 0.3505 | 0.8683 | 0.2759 | 0.2791 | 0.2775 | 0.0 | 0.0 | 0.0 | 0.8639 | 0.6755 | 0.7582 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.625 | 0.2520 | 0.3591 | 0.0 | 0.0 | 0.0 | 0.9437 | 0.7128 | 0.8121 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1714 | 0.2047 | 0.1866 | 0.0 | 0.0 | 0.0 | 0.8889 | 0.6845 | 0.7734 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2131 | 0.3615 |
| No log | 6.0 | 96 | 0.4125 | 0.3935 | 0.3613 | 0.3767 | 0.8770 | 0.3005 | 0.3241 | 0.3119 | 0.0 | 0.0 | 0.0 | 0.8387 | 0.6915 | 0.7580 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5982 | 0.4268 | 0.4982 | 0.0 | 0.0 | 0.0 | 0.9324 | 0.7340 | 0.8214 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2348 | 0.2711 | 0.2517 | 0.0 | 0.0 | 0.0 | 0.8553 | 0.6952 | 0.7670 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2338 | 0.4333 |
| No log | 7.0 | 112 | 0.4026 | 0.4013 | 0.3726 | 0.3864 | 0.8799 | 0.3111 | 0.3328 | 0.3216 | 0.0 | 0.0 | 0.0 | 0.7895 | 0.7181 | 0.7521 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6227 | 0.4236 | 0.5042 | 0.0 | 0.0 | 0.0 | 0.9342 | 0.7553 | 0.8353 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2374 | 0.2783 | 0.2562 | 0.0 | 0.0 | 0.0 | 0.8383 | 0.7487 | 0.7910 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2387 | 0.4410 |
| No log | 8.0 | 128 | 0.4034 | 0.4219 | 0.3982 | 0.4097 | 0.8846 | 0.3360 | 0.3677 | 0.3511 | 0.0 | 0.0 | 0.0 | 0.8047 | 0.7234 | 0.7619 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6198 | 0.5134 | 0.5616 | 0.0 | 0.0 | 0.0 | 0.9467 | 0.7553 | 0.8402 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2807 | 0.3160 | 0.2973 | 0.0 | 0.0 | 0.0 | 0.8343 | 0.7540 | 0.7921 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2491 | 0.4758 |
| No log | 9.0 | 144 | 0.4008 | 0.4344 | 0.4237 | 0.4290 | 0.8863 | 0.3535 | 0.3997 | 0.3752 | 0.0 | 0.0 | 0.0 | 0.7943 | 0.7394 | 0.7658 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6228 | 0.5512 | 0.5848 | 0.0 | 0.0 | 0.0 | 0.9542 | 0.7766 | 0.8563 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3119 | 0.3573 | 0.3331 | 0.0 | 0.0 | 0.0 | 0.8439 | 0.7807 | 0.8111 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2585 | 0.4994 |
| No log | 10.0 | 160 | 0.3989 | 0.4284 | 0.4258 | 0.4271 | 0.8867 | 0.3490 | 0.3997 | 0.3726 | 0.0 | 0.0 | 0.0 | 0.7705 | 0.75 | 0.7601 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6208 | 0.5543 | 0.5857 | 0.0 | 0.0 | 0.0 | 0.9545 | 0.7819 | 0.8596 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3112 | 0.3627 | 0.3350 | 0.0 | 0.0 | 0.0 | 0.8077 | 0.7861 | 0.7967 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2577 | 0.4992 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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