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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_dtanns |
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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_aanns_dtanns-seed_1024-1e-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_aann_dtanns |
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type: kanishka/counterfactual_babylm_aann_dtanns |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.4055823320854937 |
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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_aanns_dtanns-seed_1024-1e-4 |
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This model was trained from scratch on the kanishka/counterfactual_babylm_aann_dtanns dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4264 |
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- Accuracy: 0.4056 |
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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: 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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| 4.0514 | 1.0 | 18595 | 4.2435 | 0.3101 | |
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| 3.5672 | 2.0 | 37190 | 3.7652 | 0.3622 | |
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| 3.3933 | 3.0 | 55785 | 3.5859 | 0.3792 | |
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| 3.2939 | 4.0 | 74380 | 3.5397 | 0.3863 | |
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| 3.2248 | 5.0 | 92975 | 3.4728 | 0.3919 | |
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| 3.173 | 6.0 | 111570 | 3.4672 | 0.3950 | |
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| 3.1332 | 7.0 | 130165 | 3.4249 | 0.3987 | |
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| 3.0958 | 8.0 | 148760 | 3.4232 | 0.3998 | |
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| 3.0709 | 9.0 | 167355 | 3.4138 | 0.4012 | |
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| 3.0426 | 10.0 | 185950 | 3.4269 | 0.4014 | |
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| 3.0138 | 11.0 | 204545 | 3.4023 | 0.4037 | |
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| 2.995 | 12.0 | 223140 | 3.4037 | 0.4035 | |
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| 2.9702 | 13.0 | 241735 | 3.3991 | 0.4043 | |
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| 2.954 | 14.0 | 260330 | 3.4180 | 0.4042 | |
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| 2.9299 | 15.0 | 278925 | 3.4060 | 0.4049 | |
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| 2.9106 | 16.0 | 297520 | 3.4084 | 0.4049 | |
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| 2.8923 | 17.0 | 316115 | 3.4154 | 0.4055 | |
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| 2.8795 | 18.0 | 334710 | 3.4195 | 0.4057 | |
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| 2.8628 | 19.0 | 353305 | 3.4225 | 0.4057 | |
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| 2.8497 | 20.0 | 371900 | 3.4264 | 0.4056 | |
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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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