klue_ner_roberta_model

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klue_ner_roberta_model

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

• Loss: 0.0487
• Precision: 0.9546
• Recall: 0.9557
• F1: 0.9552
• Accuracy: 0.9884

Model description

Pretrained RoBERTa Model on Korean Language. See Github and Paper for more details.

Intended uses & limitations

How to use

NOTE: Use BertTokenizer instead of RobertaTokenizer. (AutoTokenizer will load BertTokenizer)

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("klue/roberta-base")
tokenizer = AutoTokenizer.from_pretrained("klue/roberta-base")

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.13.1
• Tokenizers 0.13.3