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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: roberta-base-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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# roberta-base-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: 0.7610 |
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- Acc: 0.8445 |
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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: 5 |
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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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| 0.4123 | 0.17 | 2000 | 0.4693 | 0.8332 | |
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| 0.4028 | 0.33 | 4000 | 0.4624 | 0.8338 | |
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| 0.3888 | 0.5 | 6000 | 0.4500 | 0.8375 | |
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| 0.3841 | 0.67 | 8000 | 0.4281 | 0.8416 | |
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| 0.3783 | 0.83 | 10000 | 0.4434 | 0.8365 | |
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| 0.3759 | 1.0 | 12000 | 0.4400 | 0.8418 | |
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| 0.2721 | 1.17 | 14000 | 0.5022 | 0.8427 | |
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| 0.2736 | 1.33 | 16000 | 0.5252 | 0.8431 | |
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| 0.2821 | 1.5 | 18000 | 0.4887 | 0.8409 | |
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| 0.2802 | 1.67 | 20000 | 0.4758 | 0.8458 | |
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| 0.2794 | 1.83 | 22000 | 0.4611 | 0.8458 | |
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| 0.2797 | 2.0 | 24000 | 0.4936 | 0.8456 | |
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| 0.1915 | 2.17 | 26000 | 0.5545 | 0.8462 | |
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| 0.1946 | 2.33 | 28000 | 0.5731 | 0.8443 | |
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| 0.2007 | 2.5 | 30000 | 0.5507 | 0.8428 | |
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| 0.2008 | 2.67 | 32000 | 0.5499 | 0.8454 | |
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| 0.1971 | 2.84 | 34000 | 0.5274 | 0.8483 | |
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| 0.2054 | 3.0 | 36000 | 0.5454 | 0.8476 | |
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| 0.1436 | 3.17 | 38000 | 0.6787 | 0.8442 | |
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| 0.1426 | 3.34 | 40000 | 0.6933 | 0.8421 | |
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| 0.1463 | 3.5 | 42000 | 0.6547 | 0.8455 | |
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| 0.1447 | 3.67 | 44000 | 0.6469 | 0.8438 | |
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| 0.1445 | 3.84 | 46000 | 0.6626 | 0.8472 | |
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| 0.1457 | 4.0 | 48000 | 0.6494 | 0.8504 | |
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| 0.1133 | 4.17 | 50000 | 0.7664 | 0.8459 | |
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| 0.1138 | 4.34 | 52000 | 0.7857 | 0.8452 | |
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| 0.1154 | 4.5 | 54000 | 0.7623 | 0.8486 | |
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| 0.1102 | 4.67 | 56000 | 0.7740 | 0.8460 | |
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| 0.1143 | 4.84 | 58000 | 0.7610 | 0.8445 | |
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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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