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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-1e-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: 0.40654968657553286 |
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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-1e-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: 3.4247 |
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- Accuracy: 0.4065 |
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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: 0.0001 |
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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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| 4.0532 | 1.0 | 18597 | 4.2579 | 0.3085 | |
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| 3.566 | 2.0 | 37194 | 3.7605 | 0.3620 | |
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| 3.3886 | 3.0 | 55791 | 3.5962 | 0.3806 | |
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| 3.2899 | 4.0 | 74388 | 3.5175 | 0.3894 | |
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| 3.2214 | 5.0 | 92985 | 3.4618 | 0.3939 | |
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| 3.1702 | 6.0 | 111582 | 3.4252 | 0.3979 | |
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| 3.1294 | 7.0 | 130179 | 3.4255 | 0.3995 | |
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| 3.0899 | 8.0 | 148776 | 3.4190 | 0.4010 | |
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| 3.0639 | 9.0 | 167373 | 3.4041 | 0.4027 | |
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| 3.0329 | 10.0 | 185970 | 3.4231 | 0.4029 | |
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| 3.0093 | 11.0 | 204567 | 3.4100 | 0.4045 | |
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| 2.9859 | 12.0 | 223164 | 3.4097 | 0.4049 | |
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| 2.9662 | 13.0 | 241761 | 3.4043 | 0.4053 | |
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| 2.9424 | 14.0 | 260358 | 3.4046 | 0.4057 | |
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| 2.928 | 15.0 | 278955 | 3.4079 | 0.4059 | |
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| 2.908 | 16.0 | 297552 | 3.4119 | 0.4061 | |
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| 2.8912 | 17.0 | 316149 | 3.4119 | 0.4062 | |
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| 2.8716 | 18.0 | 334746 | 3.4159 | 0.4064 | |
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| 2.8589 | 19.0 | 353343 | 3.4223 | 0.4065 | |
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| 2.8424 | 20.0 | 371940 | 3.4247 | 0.4065 | |
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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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