---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: ner_model_ep1
  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_ep1

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: 0.3469
-  allergy Name F1: 0.7059
-  allergy Name Pres: 0.7326
-  allergy Name Rec: 0.6811
-  cancer F1: 0.6499
-  cancer Pres: 0.6837
-  cancer Rec: 0.6192
-  chronic Disease F1: 0.7431
-  chronic Disease Pres: 0.7462
-  chronic Disease Rec: 0.7400
-  treatment F1: 0.7572
-  treatmen Prest: 0.7680
-  treatment Rec: 0.7468
- Over All Precision: 0.7475
- Over All Recall: 0.7237
- Over All F1: 0.7354
- Over All Accuracy: 0.8824

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:------------------:|:-----------------:|:----------:|:------------:|:-----------:|:-------------------:|:---------------------:|:--------------------:|:-------------:|:---------------:|:--------------:|:------------------:|:---------------:|:-----------:|:-----------------:|
| 0.5799        | 1.0   | 368  | 0.4111          | 0.2933           | 0.825              | 0.1784            | 0.5345     | 0.5010       | 0.5728      | 0.6044              | 0.6269                | 0.5834               | 0.6718        | 0.6294          | 0.7204         | 0.6084             | 0.6379          | 0.6228      | 0.8467            |
| 0.3846        | 2.0   | 736  | 0.3624          | 0.6618           | 0.6054             | 0.7297            | 0.6057     | 0.6025       | 0.6088      | 0.6553              | 0.6925                | 0.6219               | 0.7153        | 0.7450          | 0.6879         | 0.7                | 0.6537          | 0.6761      | 0.8642            |
| 0.3069        | 3.0   | 1104 | 0.3516          | 0.6801           | 0.7284             | 0.6378            | 0.6316     | 0.6489       | 0.6152      | 0.6994              | 0.7227                | 0.6775               | 0.7317        | 0.7368          | 0.7267         | 0.7187             | 0.6906          | 0.7044      | 0.8733            |
| 0.2571        | 4.0   | 1472 | 0.3492          | 0.6807           | 0.7687             | 0.6108            | 0.6472     | 0.6867       | 0.612       | 0.7239              | 0.7276                | 0.7201               | 0.7456        | 0.7548          | 0.7366         | 0.7358             | 0.7092          | 0.7222      | 0.8779            |
| 0.2276        | 5.0   | 1840 | 0.3469          | 0.7059           | 0.7326             | 0.6811            | 0.6499     | 0.6837       | 0.6192      | 0.7431              | 0.7462                | 0.7400               | 0.7572        | 0.7680          | 0.7468         | 0.7475             | 0.7237          | 0.7354      | 0.8824            |


### Framework versions

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