---
license: mit
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: finetuned
  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: 0.8246402877697842
    - name: Recall
      type: recall
      value: 0.8725023786869648
    - name: F1
      type: f1
      value: 0.8478964401294499
    - name: Accuracy
      type: accuracy
      value: 0.9838910991496996
---

<!-- 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. -->

# finetuned

This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0568
- Precision: 0.8246
- Recall: 0.8725
- F1: 0.8479
- Accuracy: 0.9839

## 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: 32
- eval_batch_size: 32
- 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   | 170  | 0.0582          | 0.7621    | 0.8506 | 0.8040 | 0.9816   |
| No log        | 2.0   | 340  | 0.0588          | 0.8074    | 0.8535 | 0.8298 | 0.9828   |
| 0.0712        | 3.0   | 510  | 0.0568          | 0.8246    | 0.8725 | 0.8479 | 0.9839   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.2