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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-only_other_det_removal |
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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-only_other_det_removal-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-only_other_det_removal |
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type: kanishka/counterfactual-babylm-only_other_det_removal |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.40813531756892846 |
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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-only_other_det_removal-3e-4 |
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This model was trained from scratch on the kanishka/counterfactual-babylm-only_other_det_removal dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4419 |
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- Accuracy: 0.4081 |
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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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| 3.7368 | 1.0 | 18597 | 3.9162 | 0.3452 | |
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| 3.4348 | 2.0 | 37194 | 3.6327 | 0.3748 | |
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| 3.2919 | 3.0 | 55791 | 3.5084 | 0.3900 | |
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| 3.2086 | 4.0 | 74388 | 3.4502 | 0.3956 | |
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| 3.1474 | 5.0 | 92985 | 3.4235 | 0.3995 | |
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| 3.1012 | 6.0 | 111582 | 3.4031 | 0.4020 | |
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| 3.0638 | 7.0 | 130179 | 3.4128 | 0.4030 | |
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| 3.0262 | 8.0 | 148776 | 3.3998 | 0.4046 | |
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| 3.0016 | 9.0 | 167373 | 3.3731 | 0.4070 | |
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| 2.9715 | 10.0 | 185970 | 3.4058 | 0.4062 | |
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| 2.9481 | 11.0 | 204567 | 3.3875 | 0.4069 | |
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| 2.9243 | 12.0 | 223164 | 3.4070 | 0.4070 | |
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| 2.9047 | 13.0 | 241761 | 3.4015 | 0.4079 | |
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| 2.8797 | 14.0 | 260358 | 3.4114 | 0.4077 | |
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| 2.8651 | 15.0 | 278955 | 3.4072 | 0.4083 | |
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| 2.8434 | 16.0 | 297552 | 3.4240 | 0.4075 | |
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| 2.8255 | 17.0 | 316149 | 3.4179 | 0.4083 | |
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| 2.8036 | 18.0 | 334746 | 3.4256 | 0.4082 | |
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| 2.7888 | 19.0 | 353343 | 3.4363 | 0.4083 | |
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| 2.7701 | 20.0 | 371940 | 3.4419 | 0.4081 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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