Text Classification
Transformers
TensorBoard
Safetensors
bert
HHD
10_class
multi_labels
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dynncat/bert_model_result")
model = AutoModelForSequenceClassification.from_pretrained("dynncat/bert_model_result")Quick Links
bert_model_result
This model is a fine-tuned version of beomi/kcbert-base on the unsmile_data dataset. It achieves the following results on the evaluation set:
- Loss: 0.1646
- Lrap: 0.8742
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 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: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Lrap |
|---|---|---|---|---|
| No log | 1.0 | 235 | 0.1287 | 0.8744 |
| No log | 2.0 | 470 | 0.1332 | 0.8779 |
| 0.0658 | 3.0 | 705 | 0.1450 | 0.8776 |
| 0.0658 | 4.0 | 940 | 0.1533 | 0.8740 |
| 0.0319 | 5.0 | 1175 | 0.1569 | 0.8757 |
| 0.0319 | 6.0 | 1410 | 0.1632 | 0.8715 |
| 0.018 | 7.0 | 1645 | 0.1646 | 0.8742 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.3.0
- Tokenizers 0.21.0
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Model tree for dynncat/bert_model_result
Base model
beomi/kcbert-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dynncat/bert_model_result")