---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Medical-NER-finetuned-ner
  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. -->

# Medical-NER-finetuned-ner

This model is a fine-tuned version of [Clinical-AI-Apollo/Medical-NER](https://huggingface.co/Clinical-AI-Apollo/Medical-NER) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3114
- Precision: 0.7903
- Recall: 0.9005
- F1: 0.8418
- Accuracy: 0.9313

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 90   | 0.9174          | 0.4239    | 0.3613 | 0.3901 | 0.7448   |
| No log        | 2.0   | 180  | 0.6814          | 0.5257    | 0.5521 | 0.5386 | 0.7899   |
| No log        | 3.0   | 270  | 0.6262          | 0.5383    | 0.7265 | 0.6184 | 0.7974   |
| No log        | 4.0   | 360  | 0.4934          | 0.6065    | 0.7291 | 0.6622 | 0.8434   |
| No log        | 5.0   | 450  | 0.5071          | 0.6102    | 0.7946 | 0.6903 | 0.8431   |
| 0.7847        | 6.0   | 540  | 0.4195          | 0.6863    | 0.7963 | 0.7372 | 0.8744   |
| 0.7847        | 7.0   | 630  | 0.4215          | 0.6850    | 0.8386 | 0.7541 | 0.8816   |
| 0.7847        | 8.0   | 720  | 0.3807          | 0.7287    | 0.8440 | 0.7822 | 0.8985   |
| 0.7847        | 9.0   | 810  | 0.3474          | 0.7383    | 0.8479 | 0.7893 | 0.9079   |
| 0.7847        | 10.0  | 900  | 0.3259          | 0.7583    | 0.8679 | 0.8094 | 0.9135   |
| 0.7847        | 11.0  | 990  | 0.3428          | 0.7595    | 0.8812 | 0.8158 | 0.9151   |
| 0.2288        | 12.0  | 1080 | 0.3469          | 0.7568    | 0.8821 | 0.8147 | 0.9147   |
| 0.2288        | 13.0  | 1170 | 0.3211          | 0.7790    | 0.8880 | 0.8299 | 0.9257   |
| 0.2288        | 14.0  | 1260 | 0.3217          | 0.7847    | 0.8909 | 0.8344 | 0.9271   |
| 0.2288        | 15.0  | 1350 | 0.2944          | 0.7952    | 0.8941 | 0.8418 | 0.9321   |
| 0.2288        | 16.0  | 1440 | 0.3244          | 0.7822    | 0.8986 | 0.8364 | 0.9275   |
| 0.1273        | 17.0  | 1530 | 0.3153          | 0.7911    | 0.9012 | 0.8426 | 0.9307   |
| 0.1273        | 18.0  | 1620 | 0.3198          | 0.7874    | 0.9005 | 0.8402 | 0.9298   |
| 0.1273        | 19.0  | 1710 | 0.3109          | 0.7911    | 0.9012 | 0.8426 | 0.9315   |
| 0.1273        | 20.0  | 1800 | 0.3114          | 0.7903    | 0.9005 | 0.8418 | 0.9313   |


### Framework versions

- Transformers 4.30.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.13.3