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README.md
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---
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language:
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- en
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license: apache-2.0
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base_model: bert-base-multilingual-cased
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tags:
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- generated_from_trainer
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datasets:
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- tmnam20/VieGLUE
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metrics:
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- accuracy
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model-index:
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- name: bert-base-multilingual-cased-rte-100
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results:
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- task:
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name: Text Classification
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type: text-classification
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dataset:
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name: tmnam20/VieGLUE/RTE
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type: tmnam20/VieGLUE
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config: rte
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split: validation
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args: rte
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.7075812274368231
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# bert-base-multilingual-cased-rte-100
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This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the tmnam20/VieGLUE/RTE dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6350
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- Accuracy: 0.7076
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 32
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- eval_batch_size: 16
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- seed: 100
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 3.0
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### Training results
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### Framework versions
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- Transformers 4.35.2
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- Pytorch 2.2.0.dev20231203+cu121
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- Datasets 2.15.0
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- Tokenizers 0.15.0
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