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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-only_random_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-only_random_removal-seed_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-only_random_removal |
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type: kanishka/counterfactual-babylm-only_random_removal |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.41051635038682427 |
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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-only_random_removal-seed_1024-1e-3 |
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This model was trained from scratch on the kanishka/counterfactual-babylm-only_random_removal dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4109 |
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- Accuracy: 0.4105 |
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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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| 3.6055 | 1.0 | 18588 | 3.7771 | 0.3590 | |
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| 3.3861 | 2.0 | 37176 | 3.5828 | 0.3803 | |
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| 3.2583 | 3.0 | 55764 | 3.5065 | 0.3913 | |
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| 3.176 | 4.0 | 74352 | 3.4318 | 0.3973 | |
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| 3.1227 | 5.0 | 92940 | 3.4132 | 0.4013 | |
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| 3.0828 | 6.0 | 111528 | 3.3847 | 0.4036 | |
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| 3.0461 | 7.0 | 130116 | 3.3778 | 0.4051 | |
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| 3.0138 | 8.0 | 148704 | 3.3612 | 0.4069 | |
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| 2.9878 | 9.0 | 167292 | 3.3629 | 0.4078 | |
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| 2.9634 | 10.0 | 185880 | 3.3489 | 0.4093 | |
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| 2.9347 | 11.0 | 204468 | 3.3616 | 0.4096 | |
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| 2.9136 | 12.0 | 223056 | 3.3726 | 0.4097 | |
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| 2.8947 | 13.0 | 241644 | 3.3682 | 0.4099 | |
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| 2.8789 | 14.0 | 260232 | 3.3817 | 0.4099 | |
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| 2.8559 | 15.0 | 278820 | 3.3847 | 0.4099 | |
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| 2.8374 | 16.0 | 297408 | 3.3835 | 0.4102 | |
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| 2.8117 | 17.0 | 315996 | 3.3940 | 0.4100 | |
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| 2.7969 | 18.0 | 334584 | 3.4024 | 0.4102 | |
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| 2.7772 | 19.0 | 353172 | 3.4032 | 0.4105 | |
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| 2.76 | 20.0 | 371760 | 3.4109 | 0.4105 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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