Text Classification
Transformers
TensorBoard
Safetensors
bert
HHD
10_class
multi_labels
Generated from Trainer
text-embeddings-inference
Instructions to use heado/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use heado/model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="heado/model_output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("heado/model_output") model = AutoModelForSequenceClassification.from_pretrained("heado/model_output") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: beomi/kcbert-base | |
| tags: | |
| - HHD | |
| - 10_class | |
| - multi_labels | |
| - generated_from_trainer | |
| model-index: | |
| - name: model_output | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # model_output | |
| This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the unsmile_data dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1279 | |
| - Lrap: 0.8809 | |
| ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 5 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Lrap | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| | |
| | No log | 1.0 | 235 | 0.1465 | 0.8595 | | |
| | No log | 2.0 | 470 | 0.1262 | 0.8747 | | |
| | 0.1719 | 3.0 | 705 | 0.1216 | 0.8843 | | |
| | 0.1719 | 4.0 | 940 | 0.1265 | 0.8806 | | |
| | 0.0784 | 5.0 | 1175 | 0.1279 | 0.8809 | | |
| ### Framework versions | |
| - Transformers 4.44.2 | |
| - Pytorch 2.5.0+cu121 | |
| - Datasets 3.0.2 | |
| - Tokenizers 0.19.1 | |