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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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model-index: |
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- name: mnli_CollSgE |
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results: [] |
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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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# mnli_CollSgE |
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This model is a fine-tuned version of [WillHeld/roberta-base-mnli](https://huggingface.co/WillHeld/roberta-base-mnli) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5544 |
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- Acc: 0.8501 |
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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: 32 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Acc | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 0.4124 | 0.17 | 2000 | 0.4689 | 0.8311 | |
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| 0.4013 | 0.33 | 4000 | 0.4615 | 0.8321 | |
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| 0.3867 | 0.5 | 6000 | 0.4457 | 0.8369 | |
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| 0.3807 | 0.67 | 8000 | 0.4278 | 0.8422 | |
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| 0.3728 | 0.83 | 10000 | 0.4383 | 0.8404 | |
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| 0.3702 | 1.0 | 12000 | 0.4330 | 0.8437 | |
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| 0.2664 | 1.17 | 14000 | 0.5176 | 0.8424 | |
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| 0.2657 | 1.33 | 16000 | 0.5198 | 0.8433 | |
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| 0.2731 | 1.5 | 18000 | 0.4909 | 0.8403 | |
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| 0.2689 | 1.67 | 20000 | 0.4771 | 0.8448 | |
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| 0.2648 | 1.83 | 22000 | 0.4692 | 0.8482 | |
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| 0.2641 | 2.0 | 24000 | 0.4924 | 0.8503 | |
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| 0.1858 | 2.17 | 26000 | 0.5522 | 0.8469 | |
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| 0.1872 | 2.33 | 28000 | 0.5566 | 0.8487 | |
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| 0.1893 | 2.5 | 30000 | 0.5387 | 0.8475 | |
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| 0.1874 | 2.67 | 32000 | 0.5605 | 0.8491 | |
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| 0.1824 | 2.84 | 34000 | 0.5544 | 0.8501 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.1 |
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- Tokenizers 0.12.1 |
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