Text Classification
Transformers
TensorBoard
Safetensors
bert
10_class
multi_labels
Generated from Trainer
text-embeddings-inference
Instructions to use KSStree/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KSStree/model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KSStree/model_output")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KSStree/model_output") model = AutoModelForSequenceClassification.from_pretrained("KSStree/model_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from KSStree/model_output: direct link, hf CLI and curl.
- Browser
- Download file 1.7 kB
-
https://huggingface.co/KSStree/model_output/resolve/main/README.md
- Command line
-
hf download hf://KSStree/model_output/README.md
-
curl -L -o README.md https://huggingface.co/KSStree/model_output/resolve/main/README.md
1.7 kB
metadata
library_name: transformers
license: apache-2.0
base_model: beomi/kcbert-base
tags:
- 10_class
- multi_labels
- generated_from_trainer
model-index:
- name: model_output
results: []
model_output
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: 0.1304
- Lrap: 0.8796
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 | 0.1490 | 0.8569 |
| No log | 2.0 | 470 | 0.1295 | 0.8716 |
| 0.1743 | 3.0 | 705 | 0.1239 | 0.8827 |
| 0.1743 | 4.0 | 940 | 0.1278 | 0.8807 |
| 0.0792 | 5.0 | 1175 | 0.1304 | 0.8796 |
Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4