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Woonn
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klue_bert_base

Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
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xet
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2
How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="Woonn/klue_bert_base")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("Woonn/klue_bert_base")
model = AutoModelForSequenceClassification.from_pretrained("Woonn/klue_bert_base")
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klue_bert_base

This model is a fine-tuned version of klue/bert-base on the nsmc dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2415
  • Accuracy: 0.9056
  • F1: 0.9056

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: 2

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.2742 1.0 2344 0.2381 0.9005 0.9005
0.1865 2.0 4688 0.2415 0.9056 0.9056

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1+cu116
  • Datasets 2.9.0
  • Tokenizers 0.13.2
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Dataset used to train Woonn/klue_bert_base

Evaluation results