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README.md
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---
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license: cc-by-sa-4.0
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tags:
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- generated_from_trainer
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datasets:
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- klue
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: token_classification
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results:
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- task:
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name: Token Classification
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type: token-classification
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dataset:
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name: klue
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type: klue
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config: ner
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split: validation
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args: ner
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metrics:
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- name: Precision
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type: precision
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value: 0.5834885164494104
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- name: Recall
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type: recall
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value: 0.6555090655509066
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- name: F1
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type: f1
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value: 0.6174055829228243
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- name: Accuracy
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type: accuracy
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value: 0.9235426702611924
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# token_classification
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This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2397
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- Precision: 0.5835
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- Recall: 0.6555
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- F1: 0.6174
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- Accuracy: 0.9235
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 2
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| No log | 1.0 | 313 | 0.2569 | 0.5378 | 0.6297 | 0.5801 | 0.9190 |
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| 0.3194 | 2.0 | 626 | 0.2397 | 0.5835 | 0.6555 | 0.6174 | 0.9235 |
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### Framework versions
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- Transformers 4.29.2
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- Pytorch 2.0.1+cu118
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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