metadata

library_name: transformers
tags:
  - generated_from_trainer
datasets:
  - kanishka/babylm2-rewritten-clean-spacy_no-num-adj
metrics:
  - accuracy
model-index:
  - name: opt-babylm2-rewritten-clean-spacy_no-num-adj-earlystop-bpe_seed-42_1e-3
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: kanishka/babylm2-rewritten-clean-spacy_no-num-adj
          type: kanishka/babylm2-rewritten-clean-spacy_no-num-adj
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.4781093360218181

opt-babylm2-rewritten-clean-spacy_no-num-adj-earlystop-bpe_seed-42_1e-3

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

Loss: 2.6900
Accuracy: 0.4781

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: 42
gradient_accumulation_steps: 8
total_train_batch_size: 256
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
4.0896	1.0	2225	3.8307	0.3593
3.4325	2.0	4450	3.3145	0.4081
3.1208	3.0	6675	3.1044	0.4295
2.957	4.0	8900	2.9973	0.4396
2.8381	5.0	11125	2.9338	0.4464
2.7819	6.0	13350	2.8904	0.4508
2.7385	7.0	15575	2.8666	0.4531
2.7061	8.0	17800	2.8456	0.4559
2.6855	9.0	20025	2.8332	0.4575
2.6669	10.0	22250	2.8198	0.4586
2.6499	11.0	24475	2.8118	0.4597
2.6351	12.0	26700	2.8072	0.4601
2.6204	13.0	28925	2.8026	0.4612
2.6277	14.0	31150	2.8013	0.4613
2.6136	15.0	33375	2.7791	0.4638
2.5687	16.0	35600	2.7514	0.4676
2.5184	17.0	37825	2.7283	0.4708
2.4613	18.0	40050	2.7060	0.4740
2.3966	19.0	42275	2.6947	0.4766
2.3227	19.9913	44480	2.6900	0.4781

Framework versions

Transformers 4.48.0
Pytorch 2.5.1
Datasets 3.2.0
Tokenizers 0.21.0