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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-cadec
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-cadec
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: 0.2334
- Precision: 0.6055
- Recall: 0.6988
- F1: 0.6488
- Accuracy: 0.9250
- Adr Precision: 0.5685
- Adr Recall: 0.6992
- Adr F1: 0.6271
- Disease Precision: 0.25
- Disease Recall: 0.125
- Disease F1: 0.1667
- Drug Precision: 0.8371
- Drug Recall: 0.9069
- Drug F1: 0.8706
- Finding Precision: 0.2439
- Finding Recall: 0.3448
- Finding F1: 0.2857
- Symptom Precision: 0.5
- Symptom Recall: 0.0870
- Symptom F1: 0.1481
- B-adr Precision: 0.7596
- B-adr Recall: 0.8357
- B-adr F1: 0.7958
- B-disease Precision: 0.6
- B-disease Recall: 0.1875
- B-disease F1: 0.2857
- B-drug Precision: 0.9423
- B-drug Recall: 0.9655
- B-drug F1: 0.9538
- B-finding Precision: 0.5789
- B-finding Recall: 0.3793
- B-finding F1: 0.4583
- B-symptom Precision: 0.5
- B-symptom Recall: 0.0870
- B-symptom F1: 0.1481
- I-adr Precision: 0.5699
- I-adr Recall: 0.6782
- I-adr F1: 0.6194
- I-disease Precision: 0.3333
- I-disease Recall: 0.1379
- I-disease F1: 0.1951
- I-drug Precision: 0.8611
- I-drug Recall: 0.9118
- I-drug F1: 0.8857
- I-finding Precision: 0.3125
- I-finding Recall: 0.3704
- I-finding F1: 0.3390
- I-symptom Precision: 0.0
- I-symptom Recall: 0.0
- I-symptom F1: 0.0
- Macro Avg F1: 0.4681
- Weighted Avg F1: 0.7238
## 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 | 0.2633 | 0.5612 | 0.6348 | 0.5958 | 0.9139 | 0.5047 | 0.6436 | 0.5658 | 0.0 | 0.0 | 0.0 | 0.8148 | 0.8627 | 0.8381 | 0.0714 | 0.0345 | 0.0465 | 0.0 | 0.0 | 0.0 | 0.7530 | 0.7778 | 0.7652 | 0.0 | 0.0 | 0.0 | 0.9154 | 0.9064 | 0.9109 | 1.0 | 0.0690 | 0.1290 | 0.0 | 0.0 | 0.0 | 0.4993 | 0.6362 | 0.5595 | 0.0 | 0.0 | 0.0 | 0.8775 | 0.8775 | 0.8775 | 0.3077 | 0.1481 | 0.2 | 0.0 | 0.0 | 0.0 | 0.3442 | 0.6698 |
| No log | 2.0 | 254 | 0.2358 | 0.6 | 0.6863 | 0.6402 | 0.9240 | 0.5595 | 0.6857 | 0.6162 | 0.2222 | 0.125 | 0.16 | 0.8296 | 0.9069 | 0.8665 | 0.2647 | 0.3103 | 0.2857 | 0.0 | 0.0 | 0.0 | 0.7649 | 0.8247 | 0.7937 | 0.8333 | 0.1562 | 0.2632 | 0.9327 | 0.9557 | 0.9440 | 0.7222 | 0.4483 | 0.5532 | 0.0 | 0.0 | 0.0 | 0.5646 | 0.6709 | 0.6132 | 0.2222 | 0.1379 | 0.1702 | 0.8664 | 0.9216 | 0.8931 | 0.28 | 0.2593 | 0.2692 | 0.0 | 0.0 | 0.0 | 0.4500 | 0.7185 |
| No log | 3.0 | 381 | 0.2334 | 0.6055 | 0.6988 | 0.6488 | 0.9250 | 0.5685 | 0.6992 | 0.6271 | 0.25 | 0.125 | 0.1667 | 0.8371 | 0.9069 | 0.8706 | 0.2439 | 0.3448 | 0.2857 | 0.5 | 0.0870 | 0.1481 | 0.7596 | 0.8357 | 0.7958 | 0.6 | 0.1875 | 0.2857 | 0.9423 | 0.9655 | 0.9538 | 0.5789 | 0.3793 | 0.4583 | 0.5 | 0.0870 | 0.1481 | 0.5699 | 0.6782 | 0.6194 | 0.3333 | 0.1379 | 0.1951 | 0.8611 | 0.9118 | 0.8857 | 0.3125 | 0.3704 | 0.3390 | 0.0 | 0.0 | 0.0 | 0.4681 | 0.7238 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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