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---
tags:
- generated_from_trainer
model-index:
- name: ner_model_ep2
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. -->
# ner_model_ep2
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3762
- allergy Name F1: 0.7874
- allergy Name Pres: 0.7751
- allergy Name Rec: 0.8
- cancer F1: 0.7143
- cancer Pres: 0.7106
- cancer Rec: 0.7180
- chronic Disease F1: 0.7611
- chronic Disease Pres: 0.7583
- chronic Disease Rec: 0.7638
- treatment F1: 0.7718
- treatmen Prest: 0.7533
- treatment Rec: 0.7913
- Over All Precision: 0.7495
- Over All Recall: 0.7704
- Over All F1: 0.7598
- Over All Accuracy: 0.8780
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | allergy Name F1 | allergy Name Pres | allergy Name Rec | cancer F1 | cancer Pres | cancer Rec | chronic Disease F1 | chronic Disease Pres | chronic Disease Rec | treatment F1 | treatmen Prest | treatment Rec | Over All Precision | Over All Recall | Over All F1 | Over All Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:------------------:|:-----------------:|:----------:|:------------:|:-----------:|:-------------------:|:---------------------:|:--------------------:|:-------------:|:---------------:|:--------------:|:------------------:|:---------------:|:-----------:|:-----------------:|
| 0.4256 | 1.0 | 329 | 0.3561 | 0.7266 | 0.6896 | 0.7679 | 0.6592 | 0.6972 | 0.6251 | 0.7232 | 0.7719 | 0.6804 | 0.7377 | 0.7443 | 0.7312 | 0.7457 | 0.6974 | 0.7207 | 0.8689 |
| 0.3248 | 2.0 | 658 | 0.3547 | 0.7836 | 0.7895 | 0.7778 | 0.6873 | 0.6813 | 0.6933 | 0.7480 | 0.7509 | 0.7451 | 0.7517 | 0.7121 | 0.7959 | 0.7232 | 0.7613 | 0.7417 | 0.8723 |
| 0.26 | 3.0 | 987 | 0.3655 | 0.7599 | 0.7196 | 0.8049 | 0.6904 | 0.6764 | 0.7050 | 0.7548 | 0.7620 | 0.7477 | 0.7672 | 0.7432 | 0.7928 | 0.7393 | 0.7633 | 0.7511 | 0.8753 |
| 0.225 | 4.0 | 1316 | 0.3662 | 0.7878 | 0.7783 | 0.7975 | 0.7036 | 0.7308 | 0.6782 | 0.7603 | 0.7653 | 0.7554 | 0.7682 | 0.7504 | 0.7869 | 0.7539 | 0.7593 | 0.7566 | 0.8777 |
| 0.1968 | 5.0 | 1645 | 0.3762 | 0.7874 | 0.7751 | 0.8 | 0.7143 | 0.7106 | 0.7180 | 0.7611 | 0.7583 | 0.7638 | 0.7718 | 0.7533 | 0.7913 | 0.7495 | 0.7704 | 0.7598 | 0.8780 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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