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---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bert-base-finetuned-ner-covidmed-v3
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. -->
# bert-base-finetuned-ner-covidmed-v3
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1759
- Accuracy: 0.9552
- Precision: 0.8111
- Recall: 0.8378
- F1: 0.8238
- Age Precision: 0.9002
- Age Recall: 0.9759
- Age F1-score: 0.9365
- Date Precision: 0.9651
- Date Recall: 0.9867
- Date F1-score: 0.9758
- Gender Precision: 0.9165
- Gender Recall: 0.9502
- Gender F1-score: 0.9330
- Job Precision: 0.4444
- Job Recall: 0.4162
- Job F1-score: 0.4299
- Location Precision: 0.8167
- Location Recall: 0.8791
- Location F1-score: 0.8468
- Name Precision: 0.8328
- Name Recall: 0.7987
- Name F1-score: 0.8154
- Organization Precision: 0.6818
- Organization Recall: 0.7393
- Organization F1-score: 0.7094
- Patient Id Precision: 0.96
- Patient Id Recall: 0.9815
- Patient Id F1-score: 0.9707
- Symptom And Disease Precision: 0.6834
- Symptom And Disease Recall: 0.7544
- Symptom And Disease F1-score: 0.7172
- Transportation Precision: 0.9105
- Transportation Recall: 0.8964
- Transportation F1-score: 0.9034
- Micro avg Precision: 0.8432
- Micro avg Recall: 0.8894
- Micro avg F1-score: 0.8657
- Macro avg Precision: 0.8111
- Macro avg Recall: 0.8378
- Macro avg F1-score: 0.8238
- Weighted avg Precision: 0.8449
- Weighted avg Recall: 0.8894
- Weighted avg F1-score: 0.8663
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Age Precision | Age Recall | Age F1-score | Date Precision | Date Recall | Date F1-score | Gender Precision | Gender Recall | Gender F1-score | Job Precision | Job Recall | Job F1-score | Location Precision | Location Recall | Location F1-score | Name Precision | Name Recall | Name F1-score | Organization Precision | Organization Recall | Organization F1-score | Patient Id Precision | Patient Id Recall | Patient Id F1-score | Symptom And Disease Precision | Symptom And Disease Recall | Symptom And Disease F1-score | Transportation Precision | Transportation Recall | Transportation F1-score | Micro avg Precision | Micro avg Recall | Micro avg F1-score | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------------:|:----------:|:------------:|:--------------:|:-----------:|:-------------:|:----------------:|:-------------:|:---------------:|:-------------:|:----------:|:------------:|:------------------:|:---------------:|:-----------------:|:--------------:|:-----------:|:-------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:-----------------:|:-------------------:|:-----------------------------:|:--------------------------:|:----------------------------:|:------------------------:|:---------------------:|:-----------------------:|:-------------------:|:----------------:|:------------------:|:-------------------:|:----------------:|:------------------:|:----------------------:|:-------------------:|:---------------------:|
| No log | 1.0 | 79 | 0.4545 | 0.8761 | 0.5390 | 0.4408 | 0.4548 | 0.9162 | 0.8076 | 0.8584 | 0.9329 | 0.9674 | 0.9498 | 0.9947 | 0.4026 | 0.5732 | 0.0 | 0.0 | 0.0 | 0.4892 | 0.6845 | 0.5706 | 0.0 | 0.0 | 0.0 | 0.1727 | 0.1855 | 0.1789 | 0.7334 | 0.9716 | 0.8359 | 0.2827 | 0.0836 | 0.1291 | 0.8676 | 0.3057 | 0.4521 | 0.6025 | 0.6426 | 0.6219 | 0.5390 | 0.4408 | 0.4548 | 0.5795 | 0.6426 | 0.5895 |
| No log | 2.0 | 158 | 0.2802 | 0.9247 | 0.6951 | 0.6739 | 0.6795 | 0.8503 | 0.9467 | 0.8959 | 0.9489 | 0.9764 | 0.9625 | 0.9442 | 0.8788 | 0.9103 | 0.0 | 0.0 | 0.0 | 0.6492 | 0.7951 | 0.7148 | 0.9211 | 0.6604 | 0.7692 | 0.4086 | 0.3943 | 0.4013 | 0.8775 | 0.9751 | 0.9237 | 0.5778 | 0.5264 | 0.5509 | 0.7740 | 0.5855 | 0.6667 | 0.7367 | 0.7911 | 0.7629 | 0.6951 | 0.6739 | 0.6795 | 0.7292 | 0.7911 | 0.7558 |
| No log | 3.0 | 237 | 0.2240 | 0.9387 | 0.7115 | 0.7248 | 0.7169 | 0.8728 | 0.9553 | 0.9122 | 0.9583 | 0.9861 | 0.9720 | 0.9226 | 0.9026 | 0.9125 | 0.0 | 0.0 | 0.0 | 0.7230 | 0.8253 | 0.7708 | 0.8791 | 0.7547 | 0.8122 | 0.5168 | 0.5175 | 0.5172 | 0.9230 | 0.9741 | 0.9478 | 0.5992 | 0.6540 | 0.6254 | 0.7198 | 0.6788 | 0.6987 | 0.7821 | 0.8296 | 0.8051 | 0.7115 | 0.7248 | 0.7169 | 0.7736 | 0.8296 | 0.7998 |
| No log | 4.0 | 316 | 0.2049 | 0.9414 | 0.7569 | 0.7707 | 0.7521 | 0.8650 | 0.9691 | 0.9141 | 0.9702 | 0.9849 | 0.9775 | 0.9281 | 0.9221 | 0.9251 | 0.44 | 0.1272 | 0.1973 | 0.7257 | 0.8543 | 0.7848 | 0.8507 | 0.7704 | 0.8086 | 0.5489 | 0.5901 | 0.5687 | 0.9469 | 0.9776 | 0.9620 | 0.5176 | 0.7394 | 0.6089 | 0.7760 | 0.7720 | 0.7740 | 0.7715 | 0.8593 | 0.8131 | 0.7569 | 0.7707 | 0.7521 | 0.7811 | 0.8593 | 0.8147 |
| No log | 5.0 | 395 | 0.1930 | 0.9460 | 0.7398 | 0.8172 | 0.7750 | 0.8228 | 0.9811 | 0.8950 | 0.9589 | 0.9873 | 0.9729 | 0.8519 | 0.9589 | 0.9022 | 0.2796 | 0.3006 | 0.2897 | 0.7646 | 0.8593 | 0.8092 | 0.7885 | 0.7736 | 0.7810 | 0.5462 | 0.7211 | 0.6216 | 0.9381 | 0.9820 | 0.9596 | 0.5961 | 0.7482 | 0.6635 | 0.8513 | 0.8601 | 0.8557 | 0.7838 | 0.8779 | 0.8282 | 0.7398 | 0.8172 | 0.7750 | 0.7922 | 0.8779 | 0.8318 |
| No log | 6.0 | 474 | 0.1787 | 0.9526 | 0.7912 | 0.8224 | 0.8048 | 0.8698 | 0.9759 | 0.9198 | 0.9640 | 0.9879 | 0.9758 | 0.9062 | 0.9416 | 0.9236 | 0.4351 | 0.3295 | 0.3750 | 0.8163 | 0.8606 | 0.8379 | 0.8092 | 0.7736 | 0.7910 | 0.6345 | 0.7497 | 0.6873 | 0.9512 | 0.9810 | 0.9659 | 0.6445 | 0.7438 | 0.6906 | 0.8808 | 0.8808 | 0.8808 | 0.8305 | 0.8796 | 0.8544 | 0.7912 | 0.8224 | 0.8048 | 0.8330 | 0.8796 | 0.8551 |
| 0.249 | 7.0 | 553 | 0.1752 | 0.9538 | 0.8068 | 0.8268 | 0.8158 | 0.9217 | 0.9708 | 0.9456 | 0.9668 | 0.9873 | 0.9770 | 0.9236 | 0.9416 | 0.9325 | 0.4362 | 0.3757 | 0.4037 | 0.8018 | 0.8710 | 0.8350 | 0.8339 | 0.7893 | 0.8110 | 0.6623 | 0.7173 | 0.6887 | 0.9572 | 0.9805 | 0.9687 | 0.6605 | 0.7535 | 0.7039 | 0.9043 | 0.8808 | 0.8924 | 0.8343 | 0.8830 | 0.8580 | 0.8068 | 0.8268 | 0.8158 | 0.8367 | 0.8830 | 0.8588 |
| 0.249 | 8.0 | 632 | 0.1741 | 0.9546 | 0.8080 | 0.8319 | 0.8190 | 0.8891 | 0.9777 | 0.9313 | 0.9663 | 0.9873 | 0.9767 | 0.9197 | 0.9416 | 0.9305 | 0.4351 | 0.3873 | 0.4098 | 0.8158 | 0.8674 | 0.8408 | 0.8523 | 0.7987 | 0.8247 | 0.6421 | 0.7471 | 0.6906 | 0.96 | 0.9815 | 0.9707 | 0.6948 | 0.7394 | 0.7164 | 0.9053 | 0.8912 | 0.8982 | 0.8412 | 0.8833 | 0.8618 | 0.8080 | 0.8319 | 0.8190 | 0.8431 | 0.8833 | 0.8624 |
| 0.249 | 9.0 | 711 | 0.1783 | 0.9556 | 0.8154 | 0.8331 | 0.8236 | 0.9171 | 0.9691 | 0.9424 | 0.9668 | 0.9873 | 0.9770 | 0.9333 | 0.9394 | 0.9364 | 0.4444 | 0.3931 | 0.4172 | 0.8240 | 0.8762 | 0.8493 | 0.8333 | 0.8019 | 0.8173 | 0.6907 | 0.7328 | 0.7111 | 0.9610 | 0.9820 | 0.9714 | 0.6785 | 0.7579 | 0.7160 | 0.9053 | 0.8912 | 0.8982 | 0.8482 | 0.8873 | 0.8673 | 0.8154 | 0.8331 | 0.8236 | 0.8496 | 0.8873 | 0.8678 |
| 0.249 | 10.0 | 790 | 0.1759 | 0.9552 | 0.8111 | 0.8378 | 0.8238 | 0.9002 | 0.9759 | 0.9365 | 0.9651 | 0.9867 | 0.9758 | 0.9165 | 0.9502 | 0.9330 | 0.4444 | 0.4162 | 0.4299 | 0.8167 | 0.8791 | 0.8468 | 0.8328 | 0.7987 | 0.8154 | 0.6818 | 0.7393 | 0.7094 | 0.96 | 0.9815 | 0.9707 | 0.6834 | 0.7544 | 0.7172 | 0.9105 | 0.8964 | 0.9034 | 0.8432 | 0.8894 | 0.8657 | 0.8111 | 0.8378 | 0.8238 | 0.8449 | 0.8894 | 0.8663 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
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