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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-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_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: 0.40681131693060796 |
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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-4 |
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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: 3.4240 |
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- Accuracy: 0.4068 |
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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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| 4.0517 | 1.0 | 18602 | 4.2617 | 0.3086 | |
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| 3.5614 | 2.0 | 37204 | 3.7325 | 0.3617 | |
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| 3.3871 | 3.0 | 55806 | 3.5926 | 0.3794 | |
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| 3.2873 | 4.0 | 74408 | 3.4903 | 0.3889 | |
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| 3.2166 | 5.0 | 93010 | 3.4705 | 0.3930 | |
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| 3.1683 | 6.0 | 111612 | 3.4386 | 0.3965 | |
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| 3.122 | 7.0 | 130214 | 3.4230 | 0.3987 | |
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| 3.0883 | 8.0 | 148816 | 3.4103 | 0.4020 | |
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| 3.059 | 9.0 | 167418 | 3.4161 | 0.4022 | |
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| 3.0294 | 10.0 | 186020 | 3.4004 | 0.4039 | |
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| 3.0081 | 11.0 | 204622 | 3.4048 | 0.4041 | |
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| 2.9849 | 12.0 | 223224 | 3.4068 | 0.4046 | |
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| 2.9618 | 13.0 | 241826 | 3.4127 | 0.4048 | |
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| 2.9398 | 14.0 | 260428 | 3.4079 | 0.4054 | |
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| 2.9226 | 15.0 | 279030 | 3.3963 | 0.4065 | |
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| 2.9009 | 16.0 | 297632 | 3.4036 | 0.4068 | |
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| 2.8845 | 17.0 | 316234 | 3.4090 | 0.4067 | |
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| 2.8685 | 18.0 | 334836 | 3.4054 | 0.4071 | |
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| 2.8513 | 19.0 | 353438 | 3.4187 | 0.4069 | |
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| 2.8368 | 20.0 | 372040 | 3.4240 | 0.4068 | |
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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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