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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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- commonsense_qa |
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metrics: |
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- accuracy |
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model_index: |
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- name: roberta-large-finetuned-csqa |
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results: |
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- dataset: |
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name: commonsense_qa |
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type: commonsense_qa |
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args: default |
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metric: |
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name: Accuracy |
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type: accuracy |
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value: 0.7330057621002197 |
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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-csqa |
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the commonsense_qa dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9146 |
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- Accuracy: 0.7330 |
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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: 1e-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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- 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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- 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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| 1.3903 | 1.0 | 609 | 0.8845 | 0.6642 | |
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| 0.8939 | 2.0 | 1218 | 0.7054 | 0.7281 | |
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| 0.6163 | 3.0 | 1827 | 0.7452 | 0.7314 | |
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| 0.4245 | 4.0 | 2436 | 0.8369 | 0.7355 | |
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| 0.3258 | 5.0 | 3045 | 0.9146 | 0.7330 | |
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### Framework versions |
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- Transformers 4.9.0 |
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- Pytorch 1.9.0 |
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- Datasets 1.10.2 |
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- Tokenizers 0.10.3 |
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