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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_measure_nps_as_singular_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_measure_nps_as_singular_new-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_measure_nps_as_singular_new |
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type: kanishka/counterfactual_babylm_measure_nps_as_singular_new |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.4110677518157195 |
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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_measure_nps_as_singular_new-1e-3 |
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This model was trained from scratch on the kanishka/counterfactual_babylm_measure_nps_as_singular_new dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4116 |
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- Accuracy: 0.4111 |
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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: 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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| 3.6071 | 1.0 | 18602 | 3.7886 | 0.3573 | |
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| 3.379 | 2.0 | 37204 | 3.5653 | 0.3798 | |
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| 3.2515 | 3.0 | 55806 | 3.4692 | 0.3918 | |
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| 3.1729 | 4.0 | 74408 | 3.4193 | 0.3983 | |
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| 3.1139 | 5.0 | 93010 | 3.3907 | 0.4026 | |
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| 3.0709 | 6.0 | 111612 | 3.3642 | 0.4043 | |
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| 3.0297 | 7.0 | 130214 | 3.3545 | 0.4067 | |
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| 2.9988 | 8.0 | 148816 | 3.3596 | 0.4080 | |
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| 2.9717 | 9.0 | 167418 | 3.3723 | 0.4087 | |
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| 2.9432 | 10.0 | 186020 | 3.3579 | 0.4093 | |
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| 2.9217 | 11.0 | 204622 | 3.3701 | 0.4098 | |
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| 2.8986 | 12.0 | 223224 | 3.3646 | 0.4103 | |
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| 2.8745 | 13.0 | 241826 | 3.3676 | 0.4105 | |
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| 2.8518 | 14.0 | 260428 | 3.3750 | 0.4110 | |
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| 2.8328 | 15.0 | 279030 | 3.3722 | 0.4111 | |
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| 2.8089 | 16.0 | 297632 | 3.3797 | 0.4115 | |
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| 2.7911 | 17.0 | 316234 | 3.3882 | 0.4109 | |
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| 2.773 | 18.0 | 334836 | 3.3951 | 0.4115 | |
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| 2.7517 | 19.0 | 353438 | 3.4023 | 0.4112 | |
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| 2.7342 | 20.0 | 372040 | 3.4116 | 0.4111 | |
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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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