# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("jin-soo/kobert-sentiment-3class-restaurant-fintuned")
model = AutoModelForSequenceClassification.from_pretrained("jin-soo/kobert-sentiment-3class-restaurant-fintuned")Quick Links
kobert-sentiment-3class-restaurant-fintuned
This model is a fine-tuned version of skt/kobert-base-v1 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5201
- Accuracy: 0.7820
- F1 Macro: 0.7577
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|---|---|---|---|---|---|
| 0.5298 | 1.0 | 7020 | 0.5178 | 0.7721 | 0.7467 |
| 0.4725 | 2.0 | 14040 | 0.5096 | 0.7817 | 0.7557 |
| 0.3954 | 3.0 | 21060 | 0.5201 | 0.7820 | 0.7577 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Downloads last month
- 4
Model tree for jin-soo/kobert-sentiment-3class-restaurant-fintuned
Base model
skt/kobert-base-v1
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jin-soo/kobert-sentiment-3class-restaurant-fintuned")