Text Classification
Transformers
Safetensors
bert
HHD
10_classes
Generated from Trainer
text-embeddings-inference
Instructions to use heado/my_unsmile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use heado/my_unsmile with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="heado/my_unsmile")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("heado/my_unsmile") model = AutoModelForSequenceClassification.from_pretrained("heado/my_unsmile") - Notebooks
- Google Colab
- Kaggle
my_unsmile
This model is a fine-tuned version of beomi/kcbert-base on the unsmile dataset. It achieves the following results on the evaluation set:
- Loss: 0.1627
- Lrap: 0.8610
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: 128
- eval_batch_size: 128
- 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Lrap |
|---|---|---|---|---|
| No log | 1.0 | 118 | 0.1877 | 0.8363 |
| No log | 2.0 | 236 | 0.1627 | 0.8610 |
Framework versions
- Transformers 5.10.2
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for heado/my_unsmile
Base model
beomi/kcbert-base