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README.md

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+---
+language:
+- en
+license: mit
+base_model: xlm-roberta-large
+tags:
+- generated_from_trainer
+datasets:
+- tmnam20/VieGLUE
+metrics:
+- accuracy
+model-index:
+- name: xlm-roberta-large-vtoc-100
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: tmnam20/VieGLUE/VTOC
+      type: tmnam20/VieGLUE
+      config: vtoc
+      split: validation
+      args: vtoc
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8749317312943746
+---
+
+<!-- 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. -->
+
+# xlm-roberta-large-vtoc-100
+
+This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the tmnam20/VieGLUE/VTOC dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4609
+- Accuracy: 0.8749
+
+## 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: 32
+- eval_batch_size: 16
+- seed: 100
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.2656        | 2.19  | 500  | 0.4849          | 0.8575   |
+
+
+### Framework versions
+
+- Transformers 4.36.0
+- Pytorch 2.1.0+cu121
+- Datasets 2.15.0
+- Tokenizers 0.15.0