Text Classification
Transformers
TensorBoard
Safetensors
bert
Lee
10_class
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("firedwood/model_output")
model = AutoModelForSequenceClassification.from_pretrained("firedwood/model_output")Quick Links
model_output
This model is a fine-tuned version of beomi/kcbert-base on the unsmil dataset. It achieves the following results on the evaluation set:
- Loss: 0.1517
- Lrap: 0.8775
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Lrap |
|---|---|---|---|---|
| No log | 1.0 | 235 | 0.1320 | 0.8689 |
| No log | 2.0 | 470 | 0.1322 | 0.8795 |
| 0.0668 | 3.0 | 705 | 0.1418 | 0.8777 |
| 0.0668 | 4.0 | 940 | 0.1526 | 0.8733 |
| 0.034 | 5.0 | 1175 | 0.1517 | 0.8775 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for firedwood/model_output
Base model
beomi/kcbert-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="firedwood/model_output")