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README.md
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---
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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: MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-finetuned-clinc
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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: 0.9319354838709677
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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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# MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-finetuned-clinc
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This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-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: 1.5252
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- Accuracy: 0.9319
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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: 0.0001
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- train_batch_size: 256
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- eval_batch_size: 256
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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: 10
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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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| No log | 1.0 | 60 | 4.6555 | 0.1887 |
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| No log | 2.0 | 120 | 3.8771 | 0.4784 |
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| No log | 3.0 | 180 | 3.2507 | 0.7352 |
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| 3.9668 | 4.0 | 240 | 2.7445 | 0.8365 |
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| 3.9668 | 5.0 | 300 | 2.3475 | 0.8865 |
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| 3.9668 | 6.0 | 360 | 2.0370 | 0.8926 |
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| 3.9668 | 7.0 | 420 | 1.8099 | 0.9145 |
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| 2.0924 | 8.0 | 480 | 1.6433 | 0.9190 |
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| 2.0924 | 9.0 | 540 | 1.5563 | 0.9281 |
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| 2.0924 | 10.0 | 600 | 1.5252 | 0.9319 |
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### Framework versions
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- Transformers 4.11.3
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- Pytorch 1.11.0
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- Datasets 1.16.1
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- Tokenizers 0.10.3
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