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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_indef_articles_with_pl_nouns_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-indef_articles_with_pl_nouns_removal-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_indef_articles_with_pl_nouns_removal |
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type: kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.41252109443859236 |
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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-indef_articles_with_pl_nouns_removal-1e-3 |
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This model was trained from scratch on the kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4176 |
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- Accuracy: 0.4125 |
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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: 16 |
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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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| 3.5148 | 1.0 | 37201 | 3.7270 | 0.3671 | |
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| 3.3074 | 2.0 | 74402 | 3.4841 | 0.3897 | |
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| 3.1988 | 3.0 | 111603 | 3.4300 | 0.3979 | |
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| 3.152 | 4.0 | 148804 | 3.3774 | 0.4050 | |
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| 3.0973 | 5.0 | 186005 | 3.3462 | 0.4090 | |
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| 3.0543 | 6.0 | 223206 | 3.3687 | 0.4064 | |
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| 3.0161 | 7.0 | 260407 | 3.3391 | 0.4114 | |
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| 2.9858 | 8.0 | 297608 | 3.3477 | 0.4105 | |
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| 2.9718 | 9.0 | 334809 | 3.3436 | 0.4112 | |
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| 2.9399 | 10.0 | 372010 | 3.3451 | 0.4121 | |
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| 2.9207 | 11.0 | 409211 | 3.3586 | 0.4130 | |
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| 2.8987 | 12.0 | 446412 | 3.3554 | 0.4123 | |
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| 2.8779 | 13.0 | 483613 | 3.3616 | 0.4130 | |
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| 2.8519 | 14.0 | 520814 | 3.3696 | 0.4129 | |
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| 2.8395 | 15.0 | 558015 | 3.3729 | 0.4128 | |
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| 2.8151 | 16.0 | 595216 | 3.3718 | 0.4140 | |
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| 2.798 | 17.0 | 632417 | 3.3858 | 0.4128 | |
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| 2.7738 | 18.0 | 669618 | 3.4080 | 0.4130 | |
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| 2.7555 | 19.0 | 706819 | 3.4067 | 0.4131 | |
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| 2.7434 | 20.0 | 744020 | 3.4176 | 0.4125 | |
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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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