---
tags:
- generated_from_trainer
datasets:
- jnlpba
metrics:
- precision
- recall
- f1
- accuracy
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
base_model: allenai/scibert_scivocab_uncased
model-index:
- name: scibert-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: 0.6737190414118119
      name: Precision
    - type: recall
      value: 0.7756869083352574
      name: Recall
    - type: f1
      value: 0.7211161792326267
      name: F1
    - type: accuracy
      value: 0.9226268866380928
      name: Accuracy
---

<!-- 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. -->

# scibert-finetuned-ner

This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the jnlpba dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4717
- Precision: 0.6737
- Recall: 0.7757
- F1: 0.7211
- Accuracy: 0.9226

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1608        | 1.0   | 2319  | 0.2431          | 0.6641    | 0.7581 | 0.7080 | 0.9250   |
| 0.103         | 2.0   | 4638  | 0.2916          | 0.6739    | 0.7803 | 0.7232 | 0.9228   |
| 0.0659        | 3.0   | 6957  | 0.3662          | 0.6796    | 0.7624 | 0.7186 | 0.9233   |
| 0.0393        | 4.0   | 9276  | 0.4222          | 0.6737    | 0.7771 | 0.7217 | 0.9225   |
| 0.025         | 5.0   | 11595 | 0.4717          | 0.6737    | 0.7757 | 0.7211 | 0.9226   |


### Framework versions

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