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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_measure_nps_as_singular_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_measure_nps_as_singular_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_measure_nps_as_singular_removal |
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type: kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.4096600918317765 |
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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_measure_nps_as_singular_removal-seed_1024-1e-3 |
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This model was trained from scratch on the kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4259 |
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- Accuracy: 0.4097 |
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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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| 3.6017 | 1.0 | 18600 | 3.7683 | 0.3593 | |
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| 3.3799 | 2.0 | 37200 | 3.5935 | 0.3790 | |
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| 3.2546 | 3.0 | 55800 | 3.4823 | 0.3915 | |
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| 3.1737 | 4.0 | 74400 | 3.4548 | 0.3978 | |
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| 3.1178 | 5.0 | 93000 | 3.4163 | 0.4014 | |
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| 3.0736 | 6.0 | 111600 | 3.4017 | 0.4038 | |
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| 3.0385 | 7.0 | 130200 | 3.3798 | 0.4057 | |
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| 3.0068 | 8.0 | 148800 | 3.3988 | 0.4060 | |
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| 2.9774 | 9.0 | 167400 | 3.3728 | 0.4074 | |
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| 2.9558 | 10.0 | 186000 | 3.3695 | 0.4087 | |
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| 2.9289 | 11.0 | 204600 | 3.3649 | 0.4094 | |
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| 2.9058 | 12.0 | 223200 | 3.3604 | 0.4095 | |
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| 2.8805 | 13.0 | 241800 | 3.3801 | 0.4098 | |
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| 2.8621 | 14.0 | 260400 | 3.3871 | 0.4095 | |
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| 2.8423 | 15.0 | 279000 | 3.3872 | 0.4096 | |
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| 2.8216 | 16.0 | 297600 | 3.3996 | 0.4097 | |
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| 2.8042 | 17.0 | 316200 | 3.3987 | 0.4101 | |
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| 2.7834 | 18.0 | 334800 | 3.4020 | 0.4101 | |
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| 2.7643 | 19.0 | 353400 | 3.4199 | 0.4097 | |
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| 2.7463 | 20.0 | 372000 | 3.4259 | 0.4097 | |
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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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