---
license: mit
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: ncbi_disease
      type: ncbi_disease
      config: ncbi_disease
      split: validation
      args: ncbi_disease
    metrics:
    - name: Precision
      type: precision
      value: 0.5537679932260796
    - name: Recall
      type: recall
      value: 0.6312741312741312
    - name: F1
      type: f1
      value: 0.5899864682002707
    - name: Accuracy
      type: accuracy
      value: 0.9586137150414252
---

<!-- 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. -->

# model

This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2138
- Precision: 0.5538
- Recall: 0.6313
- F1: 0.5900
- Accuracy: 0.9586

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2962        | 1.0   | 679  | 0.1463          | 0.4864    | 0.5010 | 0.4936 | 0.9532   |
| 0.1321        | 2.0   | 1358 | 0.1482          | 0.4794    | 0.5946 | 0.5308 | 0.9549   |
| 0.0649        | 3.0   | 2037 | 0.1570          | 0.5307    | 0.6168 | 0.5705 | 0.9577   |
| 0.0414        | 4.0   | 2716 | 0.1799          | 0.5050    | 0.6390 | 0.5641 | 0.9564   |
| 0.0316        | 5.0   | 3395 | 0.2138          | 0.5538    | 0.6313 | 0.5900 | 0.9586   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3