---
base_model: klue/roberta-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: klue-roberta-large-ner-identified
  results: []
---

<!-- 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-identified

This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co./klue/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0044
- Precision: 0.9920
- Recall: 0.9977
- F1: 0.9948
- Accuracy: 0.9990

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 61   | 0.0093          | 0.9807    | 0.9931 | 0.9869 | 0.9981   |
| No log        | 2.0   | 122  | 0.0065          | 0.9874    | 0.9931 | 0.9903 | 0.9984   |
| No log        | 3.0   | 183  | 0.0044          | 0.9920    | 0.9977 | 0.9948 | 0.9990   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1