|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- klue |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: koelectra-base-v3-discriminator-finetuned-ner |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: klue |
|
type: klue |
|
args: ner |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.6665182546749777 |
|
- name: Recall |
|
type: recall |
|
value: 0.7350073648032546 |
|
- name: F1 |
|
type: f1 |
|
value: 0.6990893625537877 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9395764497172635 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# koelectra-base-v3-discriminator-finetuned-ner |
|
|
|
This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on the klue dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1957 |
|
- Precision: 0.6665 |
|
- Recall: 0.7350 |
|
- F1: 0.6991 |
|
- Accuracy: 0.9396 |
|
|
|
## 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: 48 |
|
- eval_batch_size: 48 |
|
- 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 | 438 | 0.2588 | 0.5701 | 0.6655 | 0.6141 | 0.9212 | |
|
| 0.4333 | 2.0 | 876 | 0.2060 | 0.6671 | 0.7134 | 0.6895 | 0.9373 | |
|
| 0.1944 | 3.0 | 1314 | 0.1957 | 0.6665 | 0.7350 | 0.6991 | 0.9396 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.11.3 |
|
- Pytorch 1.12.0+cu102 |
|
- Datasets 1.14.0 |
|
- Tokenizers 0.10.3 |
|
|
