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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_excess_adj_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-adj_num_freq_balanced-3e-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_excess_adj_removal |
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type: kanishka/counterfactual_babylm_aann_excess_adj_removal |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.40517314741870225 |
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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-adj_num_freq_balanced-3e-4 |
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This model was trained from scratch on the kanishka/counterfactual_babylm_aann_excess_adj_removal dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4663 |
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- Accuracy: 0.4052 |
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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: 0.0003 |
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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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| 3.7291 | 1.0 | 18629 | 3.9229 | 0.3464 | |
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| 3.4237 | 2.0 | 37258 | 3.6542 | 0.3747 | |
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| 3.2842 | 3.0 | 55887 | 3.5183 | 0.3880 | |
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| 3.1952 | 4.0 | 74516 | 3.4748 | 0.3938 | |
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| 3.1351 | 5.0 | 93145 | 3.4493 | 0.3972 | |
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| 3.0844 | 6.0 | 111774 | 3.4156 | 0.4005 | |
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| 3.0442 | 7.0 | 130403 | 3.3854 | 0.4032 | |
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| 3.008 | 8.0 | 149032 | 3.4062 | 0.4030 | |
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| 2.9768 | 9.0 | 167661 | 3.3970 | 0.4047 | |
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| 2.9498 | 10.0 | 186290 | 3.4024 | 0.4047 | |
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| 2.917 | 11.0 | 204919 | 3.4242 | 0.4039 | |
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| 2.9005 | 12.0 | 223548 | 3.4093 | 0.4049 | |
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| 2.8747 | 13.0 | 242177 | 3.4192 | 0.4051 | |
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| 2.8542 | 14.0 | 260806 | 3.4233 | 0.4053 | |
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| 2.8326 | 15.0 | 279435 | 3.4314 | 0.4054 | |
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| 2.8125 | 16.0 | 298064 | 3.4404 | 0.4052 | |
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| 2.7911 | 17.0 | 316693 | 3.4450 | 0.4054 | |
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| 2.7682 | 18.0 | 335322 | 3.4488 | 0.4054 | |
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| 2.7512 | 19.0 | 353951 | 3.4581 | 0.4054 | |
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| 2.7331 | 20.0 | 372580 | 3.4663 | 0.4052 | |
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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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