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---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2315
- Precision: 0.5909
- Recall: 0.6789
- F1: 0.6318
- Accuracy: 0.9259
- Adr Precision: 0.5587
- Adr Recall: 0.6872
- Adr F1: 0.6163
- Disease Precision: 0.05
- Disease Recall: 0.0312
- Disease F1: 0.0385
- Drug Precision: 0.8364
- Drug Recall: 0.9020
- Drug F1: 0.8679
- Finding Precision: 0.1389
- Finding Recall: 0.1724
- Finding F1: 0.1538
- Symptom Precision: 0.0
- Symptom Recall: 0.0
- Symptom F1: 0.0
- B-adr Precision: 0.7568
- B-adr Recall: 0.8279
- B-adr F1: 0.7907
- B-disease Precision: 0.5
- B-disease Recall: 0.0312
- B-disease F1: 0.0588
- B-drug Precision: 0.9194
- B-drug Recall: 0.9557
- B-drug F1: 0.9372
- B-finding Precision: 0.5417
- B-finding Recall: 0.4483
- B-finding F1: 0.4906
- B-symptom Precision: 0.0
- B-symptom Recall: 0.0
- B-symptom F1: 0.0
- I-adr Precision: 0.5747
- I-adr Recall: 0.6892
- I-adr F1: 0.6268
- I-disease Precision: 0.3684
- I-disease Recall: 0.2414
- I-disease F1: 0.2917
- I-drug Precision: 0.8732
- I-drug Recall: 0.9118
- I-drug F1: 0.8921
- I-finding Precision: 0.3043
- I-finding Recall: 0.2593
- I-finding F1: 0.2800
- I-symptom Precision: 0.0
- I-symptom Recall: 0.0
- I-symptom F1: 0.0
- Macro Avg F1: 0.4368
- Weighted Avg F1: 0.7182
## 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: 3
### 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 | 127 | 0.2637 | 0.5378 | 0.6338 | 0.5819 | 0.9129 | 0.4869 | 0.6451 | 0.5550 | 0.0 | 0.0 | 0.0 | 0.7828 | 0.8480 | 0.8141 | 0.125 | 0.0690 | 0.0889 | 0.0 | 0.0 | 0.0 | 0.7377 | 0.7746 | 0.7557 | 0.0 | 0.0 | 0.0 | 0.8927 | 0.9015 | 0.8971 | 1.0 | 0.0690 | 0.1290 | 0.0 | 0.0 | 0.0 | 0.4813 | 0.6362 | 0.5480 | 0.0 | 0.0 | 0.0 | 0.8719 | 0.8676 | 0.8698 | 0.1875 | 0.1111 | 0.1395 | 0.0 | 0.0 | 0.0 | 0.3339 | 0.6592 |
| No log | 2.0 | 254 | 0.2329 | 0.5826 | 0.6621 | 0.6198 | 0.9242 | 0.5455 | 0.6677 | 0.6004 | 0.0455 | 0.0312 | 0.0370 | 0.8326 | 0.9020 | 0.8659 | 0.0769 | 0.0690 | 0.0727 | 0.0 | 0.0 | 0.0 | 0.7555 | 0.8075 | 0.7806 | 1.0 | 0.0312 | 0.0606 | 0.9159 | 0.9655 | 0.9400 | 0.6 | 0.3103 | 0.4091 | 0.0 | 0.0 | 0.0 | 0.5677 | 0.6819 | 0.6196 | 0.2727 | 0.2069 | 0.2353 | 0.8846 | 0.9020 | 0.8932 | 0.2667 | 0.1481 | 0.1905 | 0.0 | 0.0 | 0.0 | 0.4129 | 0.7090 |
| No log | 3.0 | 381 | 0.2315 | 0.5909 | 0.6789 | 0.6318 | 0.9259 | 0.5587 | 0.6872 | 0.6163 | 0.05 | 0.0312 | 0.0385 | 0.8364 | 0.9020 | 0.8679 | 0.1389 | 0.1724 | 0.1538 | 0.0 | 0.0 | 0.0 | 0.7568 | 0.8279 | 0.7907 | 0.5 | 0.0312 | 0.0588 | 0.9194 | 0.9557 | 0.9372 | 0.5417 | 0.4483 | 0.4906 | 0.0 | 0.0 | 0.0 | 0.5747 | 0.6892 | 0.6268 | 0.3684 | 0.2414 | 0.2917 | 0.8732 | 0.9118 | 0.8921 | 0.3043 | 0.2593 | 0.2800 | 0.0 | 0.0 | 0.0 | 0.4368 | 0.7182 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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