Text Classification
Transformers
Korean
electra
KoELECTRA
Korean-NLP
topic-classification
news-classification
Generated from Trainer
Instructions to use bongbongbong/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bongbongbong/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bongbongbong/tmp_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bongbongbong/tmp_trainer") model = AutoModelForSequenceClassification.from_pretrained("bongbongbong/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from bongbongbong/tmp_trainer: direct link, hf CLI and curl.
- Browser
- Download file 1.31 kB
-
https://huggingface.co/bongbongbong/tmp_trainer/resolve/main/README.md
- Command line
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hf download hf://bongbongbong/tmp_trainer/README.md
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curl -L -o README.md https://huggingface.co/bongbongbong/tmp_trainer/resolve/main/README.md
1.31 kB
metadata
library_name: transformers
language:
- ko
license: apache-2.0
base_model: monologg/koelectra-base-v3-discriminator
tags:
- text-classification
- KoELECTRA
- Korean-NLP
- topic-classification
- news-classification
- generated_from_trainer
model-index:
- name: tmp_trainer
results: []
tmp_trainer
This model is a fine-tuned version of monologg/koelectra-base-v3-discriminator on the klue-ynat dataset.
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 3.0
Framework versions
- Transformers 4.54.1
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4