---
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