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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: 0.3506
- Precision: 0.5141
- Recall: 0.5609
- F1: 0.5365
- Accuracy: 0.9010
- Adr Precision: 0.4552
- Adr Recall: 0.5683
- Adr F1: 0.5055
- Disease Precision: 0.0
- Disease Recall: 0.0
- Disease F1: 0.0
- Drug Precision: 0.7659
- Drug Recall: 0.8351
- Drug F1: 0.7990
- 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: 0.6573
- B-adr Recall: 0.7339
- B-adr F1: 0.6935
- B-disease Precision: 0.0
- B-disease Recall: 0.0
- B-disease F1: 0.0
- B-drug Precision: 0.9318
- B-drug Recall: 0.8723
- B-drug F1: 0.9011
- 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: 0.4521
- I-adr Recall: 0.5422
- I-adr F1: 0.4931
- I-disease Precision: 0.0
- I-disease Recall: 0.0
- I-disease F1: 0.0
- I-drug Precision: 0.7960
- I-drug Recall: 0.8556
- I-drug F1: 0.8247
- 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: 0.2912
- Weighted Avg F1: 0.5966
## 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 | 26 | 0.6925 | 0.0 | 0.0 | 0.0 | 0.7877 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 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 | 52 | 0.4818 | 0.3775 | 0.2508 | 0.3014 | 0.8527 | 0.2356 | 0.1788 | 0.2033 | 0.0 | 0.0 | 0.0 | 0.9606 | 0.6489 | 0.7746 | 0.0 | 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 | 0.0632 | 0.0592 | 0.0612 | 0.0 | 0.0 | 0.0 | 0.9685 | 0.6578 | 0.7834 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1634 | 0.1890 |
| No log | 3.0 | 78 | 0.4090 | 0.3740 | 0.3234 | 0.3469 | 0.8761 | 0.2779 | 0.2747 | 0.2763 | 0.0 | 0.0 | 0.0 | 0.7697 | 0.6755 | 0.7195 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6431 | 0.2583 | 0.3685 | 0.0 | 0.0 | 0.0 | 0.9524 | 0.7447 | 0.8358 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1645 | 0.1813 | 0.1725 | 0.0 | 0.0 | 0.0 | 0.8375 | 0.7166 | 0.7723 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2149 | 0.3628 |
| No log | 4.0 | 104 | 0.3834 | 0.4128 | 0.3562 | 0.3824 | 0.8813 | 0.3096 | 0.3038 | 0.3067 | 0.0 | 0.0 | 0.0 | 0.8274 | 0.7394 | 0.7809 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6346 | 0.4047 | 0.4942 | 0.0 | 0.0 | 0.0 | 0.9664 | 0.7660 | 0.8546 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2382 | 0.2442 | 0.2411 | 0.0 | 0.0 | 0.0 | 0.8735 | 0.7754 | 0.8215 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2411 | 0.4379 |
| No log | 5.0 | 130 | 0.3587 | 0.4437 | 0.4882 | 0.4649 | 0.8960 | 0.3705 | 0.4782 | 0.4175 | 0.0 | 0.0 | 0.0 | 0.7914 | 0.7872 | 0.7893 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6234 | 0.6803 | 0.6506 | 0.0 | 0.0 | 0.0 | 0.9682 | 0.8085 | 0.8812 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3689 | 0.4345 | 0.3990 | 0.0 | 0.0 | 0.0 | 0.8065 | 0.8021 | 0.8043 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2735 | 0.5465 |
| No log | 6.0 | 156 | 0.3549 | 0.4600 | 0.5005 | 0.4794 | 0.8960 | 0.3876 | 0.4913 | 0.4333 | 0.0 | 0.0 | 0.0 | 0.7906 | 0.8032 | 0.7968 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6281 | 0.7102 | 0.6667 | 0.0 | 0.0 | 0.0 | 0.9625 | 0.8191 | 0.8851 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3861 | 0.4596 | 0.4197 | 0.0 | 0.0 | 0.0 | 0.8211 | 0.8342 | 0.8276 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2799 | 0.5619 |
| No log | 7.0 | 182 | 0.3583 | 0.4842 | 0.5333 | 0.5075 | 0.9013 | 0.4205 | 0.5305 | 0.4692 | 0.0 | 0.0 | 0.0 | 0.75 | 0.8298 | 0.7879 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6412 | 0.7260 | 0.6809 | 0.0 | 0.0 | 0.0 | 0.9310 | 0.8617 | 0.8950 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4238 | 0.4794 | 0.4499 | 0.0 | 0.0 | 0.0 | 0.7892 | 0.8610 | 0.8235 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2849 | 0.5774 |
| No log | 8.0 | 208 | 0.3442 | 0.4954 | 0.5455 | 0.5192 | 0.8986 | 0.4320 | 0.5451 | 0.4820 | 0.0 | 0.0 | 0.0 | 0.7670 | 0.8404 | 0.8020 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6561 | 0.7150 | 0.6843 | 0.0 | 0.0 | 0.0 | 0.9326 | 0.8830 | 0.9071 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4279 | 0.5224 | 0.4705 | 0.0 | 0.0 | 0.0 | 0.8010 | 0.8610 | 0.8299 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2892 | 0.5872 |
| No log | 9.0 | 234 | 0.3499 | 0.5075 | 0.5517 | 0.5287 | 0.9003 | 0.4480 | 0.5567 | 0.4964 | 0.0 | 0.0 | 0.0 | 0.7610 | 0.8298 | 0.7939 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6585 | 0.7228 | 0.6892 | 0.0 | 0.0 | 0.0 | 0.9314 | 0.8670 | 0.8981 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4466 | 0.5404 | 0.4890 | 0.0 | 0.0 | 0.0 | 0.7960 | 0.8556 | 0.8247 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2901 | 0.5934 |
| No log | 10.0 | 260 | 0.3506 | 0.5141 | 0.5609 | 0.5365 | 0.9010 | 0.4552 | 0.5683 | 0.5055 | 0.0 | 0.0 | 0.0 | 0.7659 | 0.8351 | 0.7990 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6573 | 0.7339 | 0.6935 | 0.0 | 0.0 | 0.0 | 0.9318 | 0.8723 | 0.9011 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4521 | 0.5422 | 0.4931 | 0.0 | 0.0 | 0.0 | 0.7960 | 0.8556 | 0.8247 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2912 | 0.5966 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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