update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- nsmc
+metrics:
+- accuracy
+- f1
+- recall
+- precision
+model-index:
+- name: kcbert-base-finetuned-nsmc
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: nsmc
+      type: nsmc
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.90198
+    - name: F1
+      type: f1
+      value: 0.9033161705233671
+    - name: Recall
+      type: recall
+      value: 0.9095062169785088
+    - name: Precision
+      type: precision
+      value: 0.8972098126812446
+---
+
+<!-- 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. -->
+
+# kcbert-base-finetuned-nsmc
+
+This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the nsmc dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4197
+- Accuracy: 0.9020
+- F1: 0.9033
+- Recall: 0.9095
+- Precision: 0.8972
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Recall | Precision |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| 0.3028        | 0.32  | 3000  | 0.2994          | 0.8769   | 0.8732 | 0.8422 | 0.9066    |
+| 0.2833        | 0.64  | 6000  | 0.2766          | 0.8880   | 0.8844 | 0.8512 | 0.9203    |
+| 0.2719        | 0.96  | 9000  | 0.2527          | 0.8980   | 0.8981 | 0.8933 | 0.9030    |
+| 0.1938        | 1.28  | 12000 | 0.2934          | 0.8969   | 0.8965 | 0.8869 | 0.9062    |
+| 0.1907        | 1.6   | 15000 | 0.3141          | 0.8992   | 0.8999 | 0.9003 | 0.8996    |
+| 0.1824        | 1.92  | 18000 | 0.3537          | 0.8986   | 0.8964 | 0.8711 | 0.9232    |
+| 0.1261        | 2.24  | 21000 | 0.4197          | 0.9020   | 0.9033 | 0.9095 | 0.8972    |
+| 0.1237        | 2.56  | 24000 | 0.4170          | 0.8995   | 0.9017 | 0.9156 | 0.8882    |
+| 0.1182        | 2.88  | 27000 | 0.4165          | 0.9020   | 0.9036 | 0.9130 | 0.8945    |
+
+
+### Framework versions
+
+- Transformers 4.11.3
+- Pytorch 1.9.1
+- Datasets 1.14.0
+- Tokenizers 0.10.3