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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-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: 0.40685132585936323 |
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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-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: 3.4203 |
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- Accuracy: 0.4069 |
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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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| 4.0459 | 1.0 | 18601 | 4.2512 | 0.3119 | |
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| 3.5647 | 2.0 | 37202 | 3.7353 | 0.3623 | |
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| 3.3872 | 3.0 | 55803 | 3.5881 | 0.3793 | |
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| 3.2888 | 4.0 | 74404 | 3.5327 | 0.3882 | |
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| 3.2221 | 5.0 | 93005 | 3.4746 | 0.3931 | |
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| 3.1699 | 6.0 | 111606 | 3.4427 | 0.3965 | |
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| 3.1314 | 7.0 | 130207 | 3.4235 | 0.3991 | |
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| 3.0928 | 8.0 | 148808 | 3.4092 | 0.4010 | |
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| 3.0595 | 9.0 | 167409 | 3.4074 | 0.4025 | |
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| 3.0344 | 10.0 | 186010 | 3.4222 | 0.4023 | |
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| 3.0028 | 11.0 | 204611 | 3.4034 | 0.4043 | |
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| 2.9831 | 12.0 | 223212 | 3.4022 | 0.4043 | |
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| 2.9626 | 13.0 | 241813 | 3.4060 | 0.4054 | |
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| 2.9442 | 14.0 | 260414 | 3.4008 | 0.4060 | |
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| 2.9257 | 15.0 | 279015 | 3.4016 | 0.4065 | |
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| 2.909 | 16.0 | 297616 | 3.4037 | 0.4065 | |
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| 2.8892 | 17.0 | 316217 | 3.4125 | 0.4063 | |
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| 2.872 | 18.0 | 334818 | 3.4132 | 0.4066 | |
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| 2.8568 | 19.0 | 353419 | 3.4158 | 0.4069 | |
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| 2.8385 | 20.0 | 372020 | 3.4203 | 0.4069 | |
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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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