---
tags:
- generated_from_trainer
model-index:
- name: ner_model_ep3
  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_ep3

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3874
-  allergy Name F1: 0.7968
-  allergy Name Pres: 0.7706
-  allergy Name Rec: 0.8249
-  cancer F1: 0.7556
-  cancer Pres: 0.7589
-  cancer Rec: 0.7524
-  chronic Disease F1: 0.7776
-  chronic Disease Pres: 0.7562
-  chronic Disease Rec: 0.8002
-  treatment F1: 0.7804
-  treatmen Prest: 0.7620
-  treatment Rec: 0.7996
- Over All Precision: 0.7596
- Over All Recall: 0.7936
- Over All F1: 0.7762
- Over All Accuracy: 0.8806

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:------------------:|:-----------------:|:----------:|:------------:|:-----------:|:-------------------:|:---------------------:|:--------------------:|:-------------:|:---------------:|:--------------:|:------------------:|:---------------:|:-----------:|:-----------------:|
| 0.3761        | 1.0   | 324  | 0.3480          | 0.7346           | 0.6720             | 0.8099            | 0.7108     | 0.7584       | 0.6688      | 0.7657              | 0.7619                | 0.7695               | 0.7700        | 0.7437          | 0.7983         | 0.7499             | 0.7687          | 0.7592      | 0.8738            |
| 0.29          | 2.0   | 648  | 0.3548          | 0.7593           | 0.7023             | 0.8263            | 0.7406     | 0.7683       | 0.7149      | 0.7710              | 0.7608                | 0.7816               | 0.7738        | 0.7435          | 0.8067         | 0.7519             | 0.7842          | 0.7677      | 0.8775            |
| 0.232         | 3.0   | 972  | 0.3579          | 0.8046           | 0.7787             | 0.8323            | 0.7446     | 0.7472       | 0.7421      | 0.7763              | 0.7568                | 0.7968               | 0.7798        | 0.7658          | 0.7944         | 0.7601             | 0.7887          | 0.7741      | 0.8809            |
| 0.1945        | 4.0   | 1296 | 0.3829          | 0.7942           | 0.7645             | 0.8263            | 0.7463     | 0.7678       | 0.7260      | 0.7749              | 0.7584                | 0.7920               | 0.7808        | 0.7683          | 0.7938         | 0.7643             | 0.7840          | 0.7741      | 0.8792            |
| 0.1734        | 5.0   | 1620 | 0.3874          | 0.7968           | 0.7706             | 0.8249            | 0.7556     | 0.7589       | 0.7524      | 0.7776              | 0.7562                | 0.8002               | 0.7804        | 0.7620          | 0.7996         | 0.7596             | 0.7936          | 0.7762      | 0.8806            |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1