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---
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license: mit
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tags:
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- generated_from_trainer
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datasets:
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- glue
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metrics:
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- accuracy
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model-index:
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- name: xlm-roberta-large-xnli-finetuned-mnli
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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: glue
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type: glue
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args: mnli
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8548888888888889
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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-xnli-finetuned-mnli
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This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the glue dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.2542
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- Accuracy: 0.8549
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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: 4
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- eval_batch_size: 4
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- seed: 42
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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: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|
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| 0.7468 | 1.0 | 2250 | 0.8551 | 0.8348 |
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| 0.567 | 2.0 | 4500 | 0.8935 | 0.8377 |
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| 0.318 | 3.0 | 6750 | 0.9892 | 0.8492 |
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| 0.1146 | 4.0 | 9000 | 1.2373 | 0.8446 |
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| 0.0383 | 5.0 | 11250 | 1.2542 | 0.8549 |
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### Framework versions
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- Transformers 4.19.4
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- Pytorch 1.11.0+cu113
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- Datasets 2.3.0
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- Tokenizers 0.12.1
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