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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: 0.3927
- Precision: 0.4635
- Recall: 0.4545
- F1: 0.4589
- Accuracy: 0.8872
- Adr Precision: 0.3794
- Adr Recall: 0.4230
- Adr F1: 0.4000
- Disease Precision: 0.0
- Disease Recall: 0.0
- Disease F1: 0.0
- Drug Precision: 0.8010
- Drug Recall: 0.8138
- Drug F1: 0.8074
- 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: 0.6204
- B-adr Recall: 0.4992
- B-adr F1: 0.5532
- B-disease Precision: 0.0
- B-disease Recall: 0.0
- B-disease F1: 0.0
- B-drug Precision: 0.9518
- B-drug Recall: 0.8404
- B-drug F1: 0.8927
- 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: 0.2903
- I-adr Recall: 0.3070
- I-adr F1: 0.2984
- I-disease Precision: 0.0
- I-disease Recall: 0.0
- I-disease F1: 0.0
- I-drug Precision: 0.8053
- I-drug Recall: 0.8182
- I-drug F1: 0.8117
- 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: 0.2556
- Weighted Avg F1: 0.4808
## 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 | 0.7835 | 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 | 0.5158 | 0.3520 | 0.2119 | 0.2645 | 0.8499 | 0.2515 | 0.1773 | 0.2080 | 0.0 | 0.0 | 0.0 | 0.8252 | 0.4521 | 0.5842 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9794 | 0.5053 | 0.6667 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0680 | 0.0592 | 0.0633 | 0.0 | 0.0 | 0.0 | 0.9681 | 0.4866 | 0.6477 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1378 | 0.1618 |
| No log | 3.0 | 57 | 0.4437 | 0.4226 | 0.3296 | 0.3703 | 0.8663 | 0.3128 | 0.2805 | 0.2958 | 0.0 | 0.0 | 0.0 | 0.8897 | 0.6862 | 0.7748 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6104 | 0.0740 | 0.1320 | 0.0 | 0.0 | 0.0 | 0.9786 | 0.7287 | 0.8354 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1073 | 0.1131 | 0.1101 | 0.0 | 0.0 | 0.0 | 0.9085 | 0.6898 | 0.7842 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1862 | 0.2578 |
| No log | 4.0 | 76 | 0.4099 | 0.4342 | 0.3582 | 0.3926 | 0.8663 | 0.3224 | 0.2994 | 0.3105 | 0.0 | 0.0 | 0.0 | 0.8623 | 0.7660 | 0.8113 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5814 | 0.0394 | 0.0737 | 0.0 | 0.0 | 0.0 | 0.9730 | 0.7660 | 0.8571 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0784 | 0.0880 | 0.0829 | 0.0 | 0.0 | 0.0 | 0.8554 | 0.7594 | 0.8045 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1818 | 0.2324 |
| No log | 5.0 | 95 | 0.3957 | 0.4368 | 0.3746 | 0.4033 | 0.8755 | 0.3369 | 0.3183 | 0.3274 | 0.0 | 0.0 | 0.0 | 0.7819 | 0.7819 | 0.7819 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6276 | 0.1937 | 0.2960 | 0.0 | 0.0 | 0.0 | 0.9560 | 0.8085 | 0.8761 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1484 | 0.1580 | 0.1530 | 0.0 | 0.0 | 0.0 | 0.7807 | 0.7807 | 0.7807 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2106 | 0.3354 |
| No log | 6.0 | 114 | 0.3902 | 0.4435 | 0.3859 | 0.4127 | 0.8801 | 0.3464 | 0.3328 | 0.3395 | 0.0 | 0.0 | 0.0 | 0.7831 | 0.7872 | 0.7851 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6364 | 0.2866 | 0.3952 | 0.0 | 0.0 | 0.0 | 0.9455 | 0.8298 | 0.8839 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2042 | 0.2101 | 0.2071 | 0.0 | 0.0 | 0.0 | 0.8 | 0.7914 | 0.7957 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2282 | 0.3913 |
| No log | 7.0 | 133 | 0.3904 | 0.4614 | 0.4278 | 0.4440 | 0.8851 | 0.3747 | 0.3910 | 0.3826 | 0.0 | 0.0 | 0.0 | 0.7926 | 0.7926 | 0.7926 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6376 | 0.4268 | 0.5113 | 0.0 | 0.0 | 0.0 | 0.9458 | 0.8351 | 0.8870 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2622 | 0.2693 | 0.2657 | 0.0 | 0.0 | 0.0 | 0.8098 | 0.7968 | 0.8032 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2467 | 0.4536 |
| No log | 8.0 | 152 | 0.3917 | 0.4598 | 0.4278 | 0.4433 | 0.8846 | 0.3696 | 0.3852 | 0.3772 | 0.0 | 0.0 | 0.0 | 0.7969 | 0.8138 | 0.8053 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6402 | 0.4063 | 0.4971 | 0.0 | 0.0 | 0.0 | 0.9405 | 0.8404 | 0.8876 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2568 | 0.2693 | 0.2629 | 0.0 | 0.0 | 0.0 | 0.8 | 0.8128 | 0.8064 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2454 | 0.4479 |
| No log | 9.0 | 171 | 0.3909 | 0.4478 | 0.4350 | 0.4413 | 0.8837 | 0.3603 | 0.3953 | 0.3770 | 0.0 | 0.0 | 0.0 | 0.7887 | 0.8138 | 0.8010 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6333 | 0.4677 | 0.5380 | 0.0 | 0.0 | 0.0 | 0.9353 | 0.8457 | 0.8883 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2633 | 0.2837 | 0.2731 | 0.0 | 0.0 | 0.0 | 0.7969 | 0.8182 | 0.8074 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2507 | 0.4663 |
| No log | 10.0 | 190 | 0.3927 | 0.4635 | 0.4545 | 0.4589 | 0.8872 | 0.3794 | 0.4230 | 0.4000 | 0.0 | 0.0 | 0.0 | 0.8010 | 0.8138 | 0.8074 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6204 | 0.4992 | 0.5532 | 0.0 | 0.0 | 0.0 | 0.9518 | 0.8404 | 0.8927 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2903 | 0.3070 | 0.2984 | 0.0 | 0.0 | 0.0 | 0.8053 | 0.8182 | 0.8117 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2556 | 0.4808 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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