Text Classification
Transformers
Safetensors
Korean
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Doowon96/bert-base-finetuned-ynat")
model = AutoModelForSequenceClassification.from_pretrained("Doowon96/bert-base-finetuned-ynat", device_map="auto")Quick Links
bert-base-finetuned-ynat
This model is a fine-tuned version of klue/bert-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3745
- F1: 0.8704
Model description
๋ด์ค ์ ๋ชฉ์ ์
๋ ฅํ๋ฉด ๋ด์ค์ ์นดํ
๊ณ ๋ฆฌ๋ฅผ ์์ธก
label_map = {
'LABEL_0': 'IT/๊ณผํ',
'LABEL_1': '๊ฒฝ์ ',
'LABEL_2': '์ฌํ',
'LABEL_3': '์ํ๋ฌธํ',
'LABEL_4': '์ธ๊ณ',
'LABEL_5': '์คํฌ์ธ ',
'LABEL_6': '์ ์น'
}
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: 256
- eval_batch_size: 256
- 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 | F1 |
|---|---|---|---|---|
| No log | 1.0 | 179 | 0.3909 | 0.8655 |
| No log | 2.0 | 358 | 0.3788 | 0.8684 |
| 0.3774 | 3.0 | 537 | 0.3629 | 0.8699 |
| 0.3774 | 4.0 | 716 | 0.3776 | 0.8667 |
| 0.3774 | 5.0 | 895 | 0.3745 | 0.8704 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for Doowon96/bert-base-finetuned-ynat
Base model
klue/bert-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Doowon96/bert-base-finetuned-ynat")