---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: ner_model_ep_all
  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_ep_all

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3739
-  allergy Name F1: 0.7755
-  allergy Name Pres: 0.76
-  allergy Name Rec: 0.7917
-  cancer F1: 0.7389
-  cancer Pres: 0.7283
-  cancer Rec: 0.7497
-  chronic Disease F1: 0.7778
-  chronic Disease Pres: 0.7676
-  chronic Disease Rec: 0.7882
-  treatment F1: 0.7918
-  treatmen Prest: 0.7837
-  treatment Rec: 0.7999
- Over All Precision: 0.7698
- Over All Recall: 0.7887
- Over All F1: 0.7792
- Over All Accuracy: 0.8803

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:------------------:|:-----------------:|:----------:|:------------:|:-----------:|:-------------------:|:---------------------:|:--------------------:|:-------------:|:---------------:|:--------------:|:------------------:|:---------------:|:-----------:|:-----------------:|
| 0.5174        | 1.0   | 1005 | 0.3949          | 0.7230           | 0.7710             | 0.6806            | 0.6254     | 0.6354       | 0.6158      | 0.6958              | 0.6914                | 0.7003               | 0.7376        | 0.7683          | 0.7093         | 0.7218             | 0.6925          | 0.7068      | 0.8570            |
| 0.3297        | 2.0   | 2010 | 0.3664          | 0.7746           | 0.7857             | 0.7639            | 0.7133     | 0.7171       | 0.7095      | 0.7509              | 0.7746                | 0.7287               | 0.7738        | 0.7834          | 0.7643         | 0.7711             | 0.7444          | 0.7576      | 0.8732            |
| 0.2691        | 3.0   | 3015 | 0.3585          | 0.7589           | 0.8364             | 0.6944            | 0.7415     | 0.7417       | 0.7412      | 0.7674              | 0.7754                | 0.7596               | 0.7819        | 0.7652          | 0.7994         | 0.7670             | 0.7748          | 0.7709      | 0.8780            |
| 0.2278        | 4.0   | 4020 | 0.3686          | 0.7717           | 0.7878             | 0.7562            | 0.7400     | 0.7170       | 0.7645      | 0.7762              | 0.7717                | 0.7807               | 0.7885        | 0.7604          | 0.8188         | 0.7588             | 0.7965          | 0.7772      | 0.8795            |
| 0.2038        | 5.0   | 5025 | 0.3739          | 0.7755           | 0.76               | 0.7917            | 0.7389     | 0.7283       | 0.7497      | 0.7778              | 0.7676                | 0.7882               | 0.7918        | 0.7837          | 0.7999         | 0.7698             | 0.7887          | 0.7792      | 0.8803            |


### Framework versions

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