metadata

library_name: transformers
tags:
  - generated_from_trainer
datasets:
  - kanishka/babylm2-rewritten-clean-spacy_no-num-adj-eval-ablation
metrics:
  - accuracy
model-index:
  - name: opt-babylm2-rewritten-clean-spacy_no-num-adj-earlystop-bpe_seed-1024_1e-3
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: kanishka/babylm2-rewritten-clean-spacy_no-num-adj-eval-ablation
          type: kanishka/babylm2-rewritten-clean-spacy_no-num-adj-eval-ablation
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.48229742655137203

opt-babylm2-rewritten-clean-spacy_no-num-adj-earlystop-bpe_seed-1024_1e-3

This model was trained from scratch on the kanishka/babylm2-rewritten-clean-spacy_no-num-adj-eval-ablation dataset. It achieves the following results on the evaluation set:

Loss: 2.6427
Accuracy: 0.4823

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.001
train_batch_size: 32
eval_batch_size: 64
seed: 1024
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 32000
num_epochs: 20.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.1046	1.0	17787	3.1574	0.4223
2.9659	2.0	35574	3.0541	0.4326
2.9018	3.0	53361	2.9921	0.4386
2.858	4.0	71148	2.9562	0.4425
2.8303	5.0	88935	2.9372	0.4447
2.8174	6.0	106722	2.9199	0.4469
2.7932	7.0	124509	2.9019	0.4488
2.776	8.0	142296	2.8863	0.4503
2.7565	9.0	160083	2.8733	0.4519
2.7316	10.0	177870	2.8549	0.4541
2.7164	11.0	195657	2.8356	0.4565
2.7009	12.0	213444	2.8189	0.4583
2.6774	13.0	231231	2.8008	0.4607
2.6515	14.0	249018	2.7832	0.4627
2.6173	15.0	266805	2.7613	0.4654
2.5889	16.0	284592	2.7395	0.4682
2.5406	17.0	302379	2.7126	0.4714
2.4929	18.0	320166	2.6857	0.4753
2.4224	19.0	337953	2.6585	0.4793
2.3296	20.0	355740	2.6427	0.4823

Framework versions

Transformers 4.48.0
Pytorch 2.6.0+cu124
Datasets 3.2.0
Tokenizers 0.21.1