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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: mit
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base_model: xlm-roberta-large
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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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- f1
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model-index:
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- name: xlm-roberta-large-qqp-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/QQP
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type: tmnam20/VieGLUE
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config: qqp
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split: validation
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args: qqp
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.6318327974276527
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- name: F1
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type: f1
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value: 0.0
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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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# xlm-roberta-large-qqp-100
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the tmnam20/VieGLUE/QQP dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6726
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- Accuracy: 0.6318
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- F1: 0.0
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- Combined Score: 0.3159
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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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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---:|:--------------:|
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| 0.6588 | 0.88 | 10000 | 0.6582 | 0.6318 | 0.0 | 0.3159 |
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| 0.6572 | 1.76 | 20000 | 0.6583 | 0.6318 | 0.0 | 0.3159 |
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| 0.6578 | 2.64 | 30000 | 0.6771 | 0.6318 | 0.0 | 0.3159 |
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### Framework versions
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- Transformers 4.36.0
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- Pytorch 2.1.0+cu121
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- Datasets 2.15.0
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- Tokenizers 0.15.0
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