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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-100
+  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: 0.8885227896760022
+---
+
+<!-- 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-100
+
+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: 0.3284
+- Accuracy: 0.8885
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.4041        | 0.15  | 500  | 0.3611          | 0.8488   |
+| 0.3784        | 0.31  | 1000 | 0.3232          | 0.8603   |
+| 0.364         | 0.46  | 1500 | 0.3128          | 0.8642   |
+| 0.364         | 0.61  | 2000 | 0.3020          | 0.8702   |
+| 0.3236        | 0.76  | 2500 | 0.2960          | 0.8768   |
+| 0.3475        | 0.92  | 3000 | 0.2895          | 0.8816   |
+| 0.252         | 1.07  | 3500 | 0.3019          | 0.8812   |
+| 0.261         | 1.22  | 4000 | 0.2783          | 0.8893   |
+| 0.2718        | 1.37  | 4500 | 0.2880          | 0.8832   |
+| 0.2407        | 1.53  | 5000 | 0.3017          | 0.8812   |
+| 0.254         | 1.68  | 5500 | 0.2775          | 0.8827   |
+| 0.2611        | 1.83  | 6000 | 0.2837          | 0.8812   |
+| 0.257         | 1.99  | 6500 | 0.2816          | 0.8852   |
+| 0.1645        | 2.14  | 7000 | 0.3323          | 0.8845   |
+| 0.1679        | 2.29  | 7500 | 0.3568          | 0.8825   |
+| 0.1643        | 2.44  | 8000 | 0.3203          | 0.8889   |
+| 0.1662        | 2.6   | 8500 | 0.3240          | 0.8878   |
+| 0.1558        | 2.75  | 9000 | 0.3302          | 0.8856   |
+| 0.1614        | 2.9   | 9500 | 0.3299          | 0.8872   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.2.0.dev20231203+cu121
+- Datasets 2.15.0
+- Tokenizers 0.15.0