|
--- |
|
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 |
|
|
