update model card README.md

README.md

@@ -0,0 +1,71 @@
+---
+license: mit
+base_model: xlm-roberta-large
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+model-index:
+- name: fine-tuned-KoreanNLI-KorNLI-with-xlm-roberta-large
+  results: []
+---
+
+<!-- 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. -->
+
+# fine-tuned-KoreanNLI-KorNLI-with-xlm-roberta-large
+
+This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4428
+- Accuracy: 0.8439
+- F1: 0.8445
+
+## 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: 1e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
+| 0.4595        | 0.5   | 3654  | 0.4630          | 0.8064   | 0.8089 |
+| 0.4138        | 1.0   | 7308  | 0.4497          | 0.8146   | 0.8165 |
+| 0.3748        | 1.5   | 10962 | 0.4280          | 0.8420   | 0.8422 |
+| 0.3687        | 2.0   | 14616 | 0.4161          | 0.8363   | 0.8376 |
+| 0.3265        | 2.5   | 18270 | 0.4209          | 0.8459   | 0.8465 |
+| 0.3392        | 3.0   | 21924 | 0.4107          | 0.8459   | 0.8453 |
+| 0.2928        | 3.5   | 25578 | 0.4479          | 0.8395   | 0.8401 |
+| 0.2975        | 4.0   | 29232 | 0.4428          | 0.8439   | 0.8445 |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 1.13.1
+- Datasets 2.14.4
+- Tokenizers 0.13.3