transformers pytorch bert token-classification generated_from_trainer dataset:jnlpba model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- jnlpba
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
widget:
- text: The widespread circular form of DNA molecules inside cells creates very serious
    topological problems during replication. Due to the helical structure of the double
    helix the parental strands of circular DNA form a link of very high order, and
    yet they have to be unlinked before the cell division.
- text: It consists of 25 exons encoding a 1,278-amino acid glycoprotein that is composed
    of 13 transmembrane domains
model-index:
- name: biobert-finetuned-ner
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: jnlpba
      type: jnlpba
      config: jnlpba
      split: train
      args: jnlpba
    metrics:
    - type: precision
      value: 0.6550939663699308
      name: Precision
    - type: recall
      value: 0.7646040175479104
      name: Recall
    - type: f1
      value: 0.7056253995312167
      name: F1
    - type: accuracy
      value: 0.9107839603371846
      name: Accuracy
---

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# biobert-finetuned-ner

This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the jnlpba dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5113
- Precision: 0.6551
- Recall: 0.7646
- F1: 0.7056
- Accuracy: 0.9108

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1815        | 1.0   | 2319  | 0.2706          | 0.6538    | 0.7704 | 0.7073 | 0.9160   |
| 0.1226        | 2.0   | 4638  | 0.3230          | 0.6524    | 0.7675 | 0.7053 | 0.9118   |
| 0.0813        | 3.0   | 6957  | 0.3974          | 0.6483    | 0.7611 | 0.7002 | 0.9101   |
| 0.0521        | 4.0   | 9276  | 0.4529          | 0.6575    | 0.7652 | 0.7073 | 0.9121   |
| 0.0356        | 5.0   | 11595 | 0.5113          | 0.6551    | 0.7646 | 0.7056 | 0.9108   |


### Framework versions

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