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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-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_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: 0.40992788926628976 |
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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-3e-4 |
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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: 3.4054 |
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- Accuracy: 0.4099 |
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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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| 3.7446 | 1.0 | 18601 | 3.9130 | 0.3462 | |
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| 3.4295 | 2.0 | 37202 | 3.6268 | 0.3752 | |
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| 3.2878 | 3.0 | 55803 | 3.5073 | 0.3878 | |
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| 3.2031 | 4.0 | 74404 | 3.4630 | 0.3948 | |
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| 3.1443 | 5.0 | 93005 | 3.4077 | 0.3994 | |
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| 3.0973 | 6.0 | 111606 | 3.3724 | 0.4028 | |
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| 3.0617 | 7.0 | 130207 | 3.3562 | 0.4062 | |
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| 3.0252 | 8.0 | 148808 | 3.3648 | 0.4059 | |
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| 2.994 | 9.0 | 167409 | 3.3582 | 0.4071 | |
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| 2.9693 | 10.0 | 186010 | 3.3688 | 0.4075 | |
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| 2.9383 | 11.0 | 204611 | 3.3513 | 0.4092 | |
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| 2.9188 | 12.0 | 223212 | 3.3659 | 0.4086 | |
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| 2.8978 | 13.0 | 241813 | 3.3581 | 0.4097 | |
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| 2.8784 | 14.0 | 260414 | 3.3657 | 0.4103 | |
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| 2.8592 | 15.0 | 279015 | 3.3693 | 0.4102 | |
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| 2.8415 | 16.0 | 297616 | 3.3867 | 0.4092 | |
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| 2.8198 | 17.0 | 316217 | 3.3790 | 0.4101 | |
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| 2.8013 | 18.0 | 334818 | 3.3924 | 0.4099 | |
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| 2.7836 | 19.0 | 353419 | 3.4014 | 0.4098 | |
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| 2.7626 | 20.0 | 372020 | 3.4054 | 0.4099 | |
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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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