Text Classification
Transformers
TensorBoard
Safetensors
bert
HHD
10_class
multi_labels
Generated from Trainer
text-embeddings-inference
Instructions to use dynncat/bert_model_result with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dynncat/bert_model_result with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dynncat/bert_model_result")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dynncat/bert_model_result") model = AutoModelForSequenceClassification.from_pretrained("dynncat/bert_model_result", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from dynncat/bert_model_result: direct link, hf CLI and curl.
- Browser
- Download file 1.82 kB
-
https://huggingface.co/dynncat/bert_model_result/resolve/main/README.md
- Command line
-
hf download hf://dynncat/bert_model_result/README.md
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curl -L -o README.md https://huggingface.co/dynncat/bert_model_result/resolve/main/README.md
1.82 kB
metadata
library_name: transformers
license: apache-2.0
base_model: beomi/kcbert-base
tags:
- HHD
- 10_class
- multi_labels
- generated_from_trainer
model-index:
- name: bert_model_result
results: []
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