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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_aann_low_variability_numeral |
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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_low_variability_numeral_1024-1e-3 |
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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_aann_low_variability_numeral |
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type: kanishka/counterfactual_babylm_aann_low_variability_numeral |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.41125847180686265 |
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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_low_variability_numeral_1024-1e-3 |
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This model was trained from scratch on the kanishka/counterfactual_babylm_aann_low_variability_numeral dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.3975 |
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- Accuracy: 0.4113 |
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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: 0.001 |
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- train_batch_size: 32 |
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- eval_batch_size: 64 |
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- seed: 1024 |
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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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| 3.598 | 1.0 | 18593 | 3.8055 | 0.3586 | |
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| 3.3819 | 2.0 | 37186 | 3.5620 | 0.3807 | |
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| 3.2622 | 3.0 | 55779 | 3.4625 | 0.3929 | |
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| 3.1788 | 4.0 | 74372 | 3.4131 | 0.3984 | |
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| 3.1251 | 5.0 | 92965 | 3.3859 | 0.4022 | |
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| 3.0765 | 6.0 | 111558 | 3.3707 | 0.4050 | |
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| 3.0458 | 7.0 | 130151 | 3.3522 | 0.4077 | |
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| 3.0116 | 8.0 | 148744 | 3.3560 | 0.4080 | |
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| 2.9832 | 9.0 | 167337 | 3.3773 | 0.4080 | |
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| 2.9565 | 10.0 | 185930 | 3.3513 | 0.4098 | |
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| 2.9381 | 11.0 | 204523 | 3.3482 | 0.4096 | |
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| 2.9171 | 12.0 | 223116 | 3.3354 | 0.4118 | |
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| 2.893 | 13.0 | 241709 | 3.3519 | 0.4111 | |
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| 2.876 | 14.0 | 260302 | 3.3632 | 0.4109 | |
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| 2.8472 | 15.0 | 278895 | 3.3530 | 0.4120 | |
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| 2.8335 | 16.0 | 297488 | 3.3727 | 0.4113 | |
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| 2.812 | 17.0 | 316081 | 3.3814 | 0.4110 | |
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| 2.7944 | 18.0 | 334674 | 3.3788 | 0.4119 | |
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| 2.7761 | 19.0 | 353267 | 3.3925 | 0.4114 | |
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| 2.7606 | 20.0 | 371860 | 3.3975 | 0.4113 | |
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### Framework versions |
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- Transformers 4.38.0 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.2 |
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