update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- klue
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: klue-roberta-large-ner
+  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: 0.7881991814461119
+    - name: Recall
+      type: recall
+      value: 0.8104790629164621
+    - name: F1
+      type: f1
+      value: 0.7991838710792959
+    - name: Accuracy
+      type: accuracy
+      value: 0.9590597627231401
+---
+
+<!-- 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-roberta-large-ner
+
+This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on the klue dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1432
+- Precision: 0.7882
+- Recall: 0.8105
+- F1: 0.7992
+- Accuracy: 0.9591
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1585        | 1.0   | 2626 | 0.1648          | 0.7517    | 0.7499 | 0.7508 | 0.9489   |
+| 0.1092        | 2.0   | 5252 | 0.1457          | 0.7776    | 0.7909 | 0.7842 | 0.9557   |
+| 0.0714        | 3.0   | 7878 | 0.1432          | 0.7882    | 0.8105 | 0.7992 | 0.9591   |
+
+
+### Framework versions
+
+- Transformers 4.26.1
+- Pytorch 1.12.1
+- Datasets 2.12.0
+- Tokenizers 0.13.2