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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-100
+  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: 0.8905515706158793
+    - name: F1
+      type: f1
+      value: 0.8513354611120443
+---
+
+<!-- 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-100
+
+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: 0.2983
+- Accuracy: 0.8906
+- F1: 0.8513
+- Combined Score: 0.8709
+
+## 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 | F1     | Combined Score |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
+| 0.3417        | 0.44  | 5000  | 0.3198          | 0.8578   | 0.8057 | 0.8317         |
+| 0.2998        | 0.88  | 10000 | 0.2908          | 0.8724   | 0.8252 | 0.8488         |
+| 0.2629        | 1.32  | 15000 | 0.2970          | 0.8763   | 0.8300 | 0.8532         |
+| 0.2269        | 1.76  | 20000 | 0.2874          | 0.8845   | 0.8405 | 0.8625         |
+| 0.1933        | 2.2   | 25000 | 0.2962          | 0.8867   | 0.8470 | 0.8669         |
+| 0.1752        | 2.64  | 30000 | 0.3174          | 0.8895   | 0.8497 | 0.8696         |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.2.0.dev20231203+cu121
+- Datasets 2.15.0
+- Tokenizers 0.15.0