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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_new |
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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_indef_articles_with_pl_nouns_removal_new-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_new |
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type: kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal_new |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.4091656007481136 |
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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_indef_articles_with_pl_nouns_removal_new-3e-4 |
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This model was trained from scratch on the kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal_new dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4250 |
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- Accuracy: 0.4092 |
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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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| 3.7386 | 1.0 | 18600 | 3.9390 | 0.3446 | |
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| 3.4323 | 2.0 | 37200 | 3.6439 | 0.3747 | |
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| 3.2906 | 3.0 | 55800 | 3.5132 | 0.3878 | |
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| 3.2008 | 4.0 | 74400 | 3.4662 | 0.3952 | |
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| 3.1424 | 5.0 | 93000 | 3.4252 | 0.3988 | |
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| 3.0983 | 6.0 | 111600 | 3.4146 | 0.4023 | |
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| 3.061 | 7.0 | 130200 | 3.3961 | 0.4039 | |
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| 3.0241 | 8.0 | 148800 | 3.3675 | 0.4061 | |
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| 2.9955 | 9.0 | 167400 | 3.3690 | 0.4071 | |
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| 2.971 | 10.0 | 186000 | 3.3668 | 0.4077 | |
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| 2.9425 | 11.0 | 204600 | 3.3717 | 0.4083 | |
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| 2.9175 | 12.0 | 223200 | 3.3836 | 0.4085 | |
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| 2.8993 | 13.0 | 241800 | 3.3685 | 0.4096 | |
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| 2.8802 | 14.0 | 260400 | 3.3869 | 0.4094 | |
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| 2.8591 | 15.0 | 279000 | 3.3903 | 0.4093 | |
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| 2.8397 | 16.0 | 297600 | 3.3899 | 0.4099 | |
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| 2.8158 | 17.0 | 316200 | 3.3992 | 0.4095 | |
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| 2.7994 | 18.0 | 334800 | 3.4129 | 0.4090 | |
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| 2.7773 | 19.0 | 353400 | 3.4211 | 0.4092 | |
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| 2.7599 | 20.0 | 372000 | 3.4250 | 0.4092 | |
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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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