---
license: mit
base_model: Clinical-AI-Apollo/Medical-NER
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: 0.0603
- Precision: 0.8160
- Recall: 0.8837
- F1: 0.8485
- Accuracy: 0.9837

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 340  | 0.0609          | 0.8136    | 0.8503 | 0.8316 | 0.9825   |
| 0.1068        | 2.0   | 680  | 0.0520          | 0.8096    | 0.8819 | 0.8442 | 0.9835   |
| 0.0335        | 3.0   | 1020 | 0.0603          | 0.8160    | 0.8837 | 0.8485 | 0.9837   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2