update model card README.md

README.md

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+---
+license: cc-by-sa-4.0
+base_model: klue/bert-base
+tags:
+- generated_from_trainer
+datasets:
+- klue
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: klue_ner_bert_model
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: klue
+      type: klue
+      config: ner
+      split: validation
+      args: ner
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.883861132284665
+    - name: Recall
+      type: recall
+      value: 0.8966608084358524
+    - name: F1
+      type: f1
+      value: 0.890214963707426
+    - name: Accuracy
+      type: accuracy
+      value: 0.9781297871646948
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# klue_ner_bert_model
+
+This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0843
+- Precision: 0.8839
+- Recall: 0.8967
+- F1: 0.8902
+- Accuracy: 0.9781
+
+## 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: 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
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0638        | 1.0   | 2626 | 0.0807          | 0.8623    | 0.8702 | 0.8662 | 0.9747   |
+| 0.0402        | 2.0   | 5252 | 0.0780          | 0.8756    | 0.8896 | 0.8825 | 0.9770   |
+| 0.025         | 3.0   | 7878 | 0.0843          | 0.8839    | 0.8967 | 0.8902 | 0.9781   |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.0
+- Tokenizers 0.13.3