Text Classification
Transformers
Safetensors
PyTorch
roberta
absa
aspect-based-sentiment-analysis
sentiment-analysis
korean
Generated from Trainer
Instructions to use cocoaice/klue-roberta-base-absa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cocoaice/klue-roberta-base-absa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cocoaice/klue-roberta-base-absa")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cocoaice/klue-roberta-base-absa") model = AutoModelForSequenceClassification.from_pretrained("cocoaice/klue-roberta-base-absa") - Notebooks
- Google Colab
- Kaggle
klue-roberta-base-absa
This model is a fine-tuned version of klue/roberta-base on an unknown dataset.
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 32
- 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
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for cocoaice/klue-roberta-base-absa
Base model
klue/roberta-base