---
license: mit
tags:
- generated_from_trainer
datasets:
- jnlpba
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pubmedbert-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: jnlpba
      type: jnlpba
      config: jnlpba
      split: train
      args: jnlpba
    metrics:
    - name: Precision
      type: precision
      value: 0.6877153861747415
    - name: Recall
      type: recall
      value: 0.7833063957515586
    - name: F1
      type: f1
      value: 0.7324050086355786
    - name: Accuracy
      type: accuracy
      value: 0.926729986431479
---

<!-- 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. -->

# pubmedbert-finetuned-ner

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 jnlpba dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3766
- Precision: 0.6877
- Recall: 0.7833
- F1: 0.7324
- Accuracy: 0.9267

## 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1607        | 1.0   | 2319  | 0.2241          | 0.6853    | 0.7835 | 0.7311 | 0.9302   |
| 0.112         | 2.0   | 4638  | 0.2620          | 0.6753    | 0.7929 | 0.7294 | 0.9276   |
| 0.0785        | 3.0   | 6957  | 0.3014          | 0.6948    | 0.7731 | 0.7319 | 0.9268   |
| 0.055         | 4.0   | 9276  | 0.3526          | 0.6898    | 0.7801 | 0.7322 | 0.9268   |
| 0.0418        | 5.0   | 11595 | 0.3766          | 0.6877    | 0.7833 | 0.7324 | 0.9267   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1