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README.md
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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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- clinc_oos
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metrics:
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- accuracy
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model-index:
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- name: roberta-large-finetuned-clinc-123
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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: clinc_oos
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type: clinc_oos
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args: plus
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.925483870967742
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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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# roberta-large-finetuned-clinc-123
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the clinc_oos dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7226
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- Accuracy: 0.9255
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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: 16
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- eval_batch_size: 16
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- seed: 42
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- distributed_type: sagemaker_data_parallel
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- num_devices: 8
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- total_train_batch_size: 128
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- total_eval_batch_size: 128
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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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- lr_scheduler_warmup_steps: 500
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- num_epochs: 3
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- mixed_precision_training: Native AMP
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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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| 5.0576 | 1.0 | 120 | 5.0269 | 0.0068 |
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| 4.5101 | 2.0 | 240 | 2.9324 | 0.7158 |
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| 1.9757 | 3.0 | 360 | 0.7226 | 0.9255 |
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### Framework versions
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- Transformers 4.17.0
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- Pytorch 1.10.2+cu113
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- Datasets 1.18.4
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- Tokenizers 0.11.6
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