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README.md

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+---
+language:
+- en
+license: apache-2.0
+base_model: bert-base-multilingual-cased
+tags:
+- generated_from_trainer
+datasets:
+- tmnam20/VieGLUE
+metrics:
+- accuracy
+model-index:
+- name: bert-base-multilingual-cased-qnli-10
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: tmnam20/VieGLUE/QNLI
+      type: tmnam20/VieGLUE
+      config: qnli
+      split: validation
+      args: qnli
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.891085484166209
+---
+
+<!-- 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. -->
+
+# bert-base-multilingual-cased-qnli-10
+
+This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the tmnam20/VieGLUE/QNLI dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3198
+- Accuracy: 0.8911
+
+## 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: 10
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.4249        | 0.15  | 500  | 0.3656          | 0.8464   |
+| 0.3989        | 0.31  | 1000 | 0.3319          | 0.8581   |
+| 0.3557        | 0.46  | 1500 | 0.3096          | 0.8688   |
+| 0.3257        | 0.61  | 2000 | 0.3055          | 0.8700   |
+| 0.3403        | 0.76  | 2500 | 0.2893          | 0.8786   |
+| 0.311         | 0.92  | 3000 | 0.2919          | 0.8841   |
+| 0.2424        | 1.07  | 3500 | 0.2974          | 0.8838   |
+| 0.2663        | 1.22  | 4000 | 0.2966          | 0.8845   |
+| 0.2486        | 1.37  | 4500 | 0.2904          | 0.8828   |
+| 0.2442        | 1.53  | 5000 | 0.2919          | 0.8810   |
+| 0.252         | 1.68  | 5500 | 0.2781          | 0.8880   |
+| 0.2514        | 1.83  | 6000 | 0.2754          | 0.8867   |
+| 0.254         | 1.99  | 6500 | 0.2692          | 0.8882   |
+| 0.1632        | 2.14  | 7000 | 0.3349          | 0.8867   |
+| 0.1835        | 2.29  | 7500 | 0.3126          | 0.8902   |
+| 0.1725        | 2.44  | 8000 | 0.3145          | 0.8902   |
+| 0.1624        | 2.6   | 8500 | 0.3272          | 0.8876   |
+| 0.1751        | 2.75  | 9000 | 0.3240          | 0.8882   |
+| 0.1653        | 2.9   | 9500 | 0.3235          | 0.8900   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.2.0.dev20231203+cu121
+- Datasets 2.15.0
+- Tokenizers 0.15.0