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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: 0.2301
- Precision: 0.5948
- Recall: 0.6779
- F1: 0.6336
- Accuracy: 0.9265
- Adr Precision: 0.5579
- Adr Recall: 0.6812
- Adr F1: 0.6134
- Disease Precision: 0.2273
- Disease Recall: 0.1562
- Disease F1: 0.1852
- Drug Precision: 0.8136
- Drug Recall: 0.8775
- Drug F1: 0.8443
- Finding Precision: 0.2667
- Finding Recall: 0.2759
- Finding F1: 0.2712
- Symptom Precision: 0.5
- Symptom Recall: 0.0435
- Symptom F1: 0.08
- B-adr Precision: 0.7749
- B-adr Recall: 0.8513
- B-adr F1: 0.8113
- B-disease Precision: 1.0
- B-disease Recall: 0.1562
- B-disease F1: 0.2703
- B-drug Precision: 0.9327
- B-drug Recall: 0.9557
- B-drug F1: 0.9440
- B-finding Precision: 0.5909
- B-finding Recall: 0.4483
- B-finding F1: 0.5098
- B-symptom Precision: 0.5
- B-symptom Recall: 0.0435
- B-symptom F1: 0.08
- I-adr Precision: 0.5725
- I-adr Recall: 0.6782
- I-adr F1: 0.6209
- I-disease Precision: 0.4091
- I-disease Recall: 0.3103
- I-disease F1: 0.3529
- I-drug Precision: 0.8458
- I-drug Recall: 0.8873
- I-drug F1: 0.8660
- I-finding Precision: 0.3529
- I-finding Recall: 0.2222
- I-finding F1: 0.2727
- I-symptom Precision: 0.0
- I-symptom Recall: 0.0
- I-symptom F1: 0.0
- Macro Avg F1: 0.4728
- Weighted Avg F1: 0.7278
## 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 | 0.2653 | 0.5472 | 0.6201 | 0.5814 | 0.9128 | 0.4942 | 0.6376 | 0.5568 | 0.0 | 0.0 | 0.0 | 0.7952 | 0.8186 | 0.8068 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7530 | 0.7731 | 0.7629 | 0.0 | 0.0 | 0.0 | 0.9179 | 0.8818 | 0.8995 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4915 | 0.6325 | 0.5532 | 0.1429 | 0.0345 | 0.0556 | 0.855 | 0.8382 | 0.8465 | 0.3333 | 0.0370 | 0.0667 | 0.0 | 0.0 | 0.0 | 0.3184 | 0.6587 |
| No log | 2.0 | 254 | 0.2307 | 0.5896 | 0.6632 | 0.6242 | 0.9254 | 0.5546 | 0.6722 | 0.6077 | 0.2222 | 0.1875 | 0.2034 | 0.8093 | 0.8529 | 0.8305 | 0.2083 | 0.1724 | 0.1887 | 0.0 | 0.0 | 0.0 | 0.7663 | 0.8263 | 0.7952 | 1.0 | 0.1562 | 0.2703 | 0.9366 | 0.9458 | 0.9412 | 0.625 | 0.3448 | 0.4444 | 0.0 | 0.0 | 0.0 | 0.5649 | 0.6600 | 0.6088 | 0.2963 | 0.2759 | 0.2857 | 0.8495 | 0.8578 | 0.8537 | 0.3846 | 0.1852 | 0.25 | 0.0 | 0.0 | 0.0 | 0.4449 | 0.7127 |
| No log | 3.0 | 381 | 0.2301 | 0.5948 | 0.6779 | 0.6336 | 0.9265 | 0.5579 | 0.6812 | 0.6134 | 0.2273 | 0.1562 | 0.1852 | 0.8136 | 0.8775 | 0.8443 | 0.2667 | 0.2759 | 0.2712 | 0.5 | 0.0435 | 0.08 | 0.7749 | 0.8513 | 0.8113 | 1.0 | 0.1562 | 0.2703 | 0.9327 | 0.9557 | 0.9440 | 0.5909 | 0.4483 | 0.5098 | 0.5 | 0.0435 | 0.08 | 0.5725 | 0.6782 | 0.6209 | 0.4091 | 0.3103 | 0.3529 | 0.8458 | 0.8873 | 0.8660 | 0.3529 | 0.2222 | 0.2727 | 0.0 | 0.0 | 0.0 | 0.4728 | 0.7278 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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