---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: klue-roberta-large-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: klue
      type: klue
      config: ner
      split: validation
      args: ner
    metrics:
    - name: Precision
      type: precision
      value: 0.7881991814461119
    - name: Recall
      type: recall
      value: 0.8104790629164621
    - name: F1
      type: f1
      value: 0.7991838710792959
    - name: Accuracy
      type: accuracy
      value: 0.9590597627231401
---

<!-- 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. -->

# klue-roberta-large-ner

This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1432
- Precision: 0.7882
- Recall: 0.8105
- F1: 0.7992
- Accuracy: 0.9591

## 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: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1585        | 1.0   | 2626 | 0.1648          | 0.7517    | 0.7499 | 0.7508 | 0.9489   |
| 0.1092        | 2.0   | 5252 | 0.1457          | 0.7776    | 0.7909 | 0.7842 | 0.9557   |
| 0.0714        | 3.0   | 7878 | 0.1432          | 0.7882    | 0.8105 | 0.7992 | 0.9591   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.2