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

<!-- 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