library_name, tags, datasets, metrics, model-index

library_name	tags	datasets	metrics	model-index
transformers	generated_from_trainer	wikitext	accuracy	name results gpt2-verification task dataset metrics name type Causal Language Modeling text-generation name type args wikitext wikitext-2-raw-v1 wikitext wikitext-2-raw-v1 name type value Accuracy accuracy 0.18546350602802217

gpt2-verification

This model utilizes the GPT2 architecture and GPT2 tokenizer, and was trained from scratch using wikitext-2-raw-v1.

It achieves the following results on the evaluation set:

• Loss: 6.0571
• Accuracy: 0.1855

Model Train Command

CUDA_VISIBLE_DEVICES=0 uv run python examples/pytorch/language-modeling/run_clm.py \
  --model_type gpt2 \
  --tokenizer_name gpt2 \
  --dataset_name wikitext \
  --dataset_config_name wikitext-2-raw-v1 \
  --per_device_train_batch_size 2 \
  --per_device_eval_batch_size 2 \
  --num_train_epochs 3 \
  --do_train \
  --do_eval \
  --output_dir ./output/gpt2-test \
  --save_steps 500 \
  --logging_steps 100 \
  --push_to_hub \
  --hub_model_id "yasutoshi-lab/gpt2-verification"
  --hub_private_repo true \
  --hub_strategy "end"

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: 5e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Framework versions

• Transformers 5.6.0.dev0
• Pytorch 2.11.0+cu128
• Datasets 4.8.4
• Tokenizers 0.22.2