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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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- kanishka/counterfactual_babylm_prototypical_only |
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metrics: |
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- accuracy |
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model-index: |
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- name: smolm-autoreg-bpe-counterfactual-babylm-aann-prototypical_only-3e-4 |
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results: |
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- task: |
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name: Causal Language Modeling |
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type: text-generation |
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dataset: |
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name: kanishka/counterfactual_babylm_prototypical_only |
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type: kanishka/counterfactual_babylm_prototypical_only |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.40804219428728983 |
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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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# smolm-autoreg-bpe-counterfactual-babylm-aann-prototypical_only-3e-4 |
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This model was trained from scratch on the kanishka/counterfactual_babylm_prototypical_only dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4082 |
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- Accuracy: 0.4080 |
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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: 0.0003 |
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- train_batch_size: 32 |
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- eval_batch_size: 64 |
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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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- lr_scheduler_warmup_steps: 32000 |
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- num_epochs: 20.0 |
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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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| 3.7341 | 1.0 | 18593 | 3.8768 | 0.3468 | |
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| 3.4322 | 2.0 | 37186 | 3.6158 | 0.3751 | |
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| 3.2902 | 3.0 | 55779 | 3.4817 | 0.3883 | |
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| 3.21 | 4.0 | 74372 | 3.4286 | 0.3960 | |
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| 3.1498 | 5.0 | 92965 | 3.4151 | 0.3978 | |
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| 3.0981 | 6.0 | 111558 | 3.3790 | 0.4022 | |
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| 3.0651 | 7.0 | 130151 | 3.3750 | 0.4034 | |
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| 3.0292 | 8.0 | 148744 | 3.3879 | 0.4041 | |
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| 3.0031 | 9.0 | 167337 | 3.3773 | 0.4046 | |
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| 2.9713 | 10.0 | 185930 | 3.3769 | 0.4061 | |
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| 2.9529 | 11.0 | 204523 | 3.3778 | 0.4069 | |
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| 2.9286 | 12.0 | 223116 | 3.3612 | 0.4077 | |
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| 2.9065 | 13.0 | 241709 | 3.3686 | 0.4073 | |
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| 2.8837 | 14.0 | 260302 | 3.3861 | 0.4078 | |
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| 2.8621 | 15.0 | 278895 | 3.3851 | 0.4077 | |
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| 2.8487 | 16.0 | 297488 | 3.3876 | 0.4080 | |
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| 2.8243 | 17.0 | 316081 | 3.3908 | 0.4081 | |
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| 2.8078 | 18.0 | 334674 | 3.3952 | 0.4082 | |
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| 2.7887 | 19.0 | 353267 | 3.4020 | 0.4082 | |
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| 2.7716 | 20.0 | 371860 | 3.4082 | 0.4080 | |
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### Framework versions |
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- Transformers 4.36.0 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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