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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
+- f1
+model-index:
+- name: bert-base-multilingual-cased-qqp-10
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: tmnam20/VieGLUE/QQP
+      type: tmnam20/VieGLUE
+      config: qqp
+      split: validation
+      args: qqp
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8885975760573831
+    - name: F1
+      type: f1
+      value: 0.8473737716028464
+---
+
+<!-- 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-qqp-10
+
+This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the tmnam20/VieGLUE/QQP dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3064
+- Accuracy: 0.8886
+- F1: 0.8474
+- Combined Score: 0.8680
+
+## 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 | F1     | Combined Score |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
+| 0.3263        | 0.44  | 5000  | 0.3272          | 0.8557   | 0.8081 | 0.8319         |
+| 0.3084        | 0.88  | 10000 | 0.2968          | 0.8680   | 0.8191 | 0.8436         |
+| 0.2424        | 1.32  | 15000 | 0.2998          | 0.8768   | 0.8324 | 0.8546         |
+| 0.2171        | 1.76  | 20000 | 0.2995          | 0.8847   | 0.8449 | 0.8648         |
+| 0.1796        | 2.2   | 25000 | 0.3124          | 0.8857   | 0.8424 | 0.8640         |
+| 0.1811        | 2.64  | 30000 | 0.2963          | 0.8883   | 0.8477 | 0.8680         |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.2.0.dev20231203+cu121
+- Datasets 2.15.0
+- Tokenizers 0.15.0