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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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- wikitext |
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model-index: |
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- name: clm_output_medium |
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results: [] |
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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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# clm_output_medium |
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This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the [wikitext-103-raw-v1](https://huggingface.co/datasets/wikitext) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.6973 |
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## Model description |
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GPT2 is a large transformer-based language model. It is built using transformer decoder blocks. BERT, on the other hand, uses transformer encoder blocks. It adds Layer normalisation to the input of each sub-block, similar to a pre-activation residual networks and an additional layer normalisation. |
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Paper link : [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf) |
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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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[wikitext-103-raw-v1](https://huggingface.co/datasets/wikitext) dataset |
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## Training procedure |
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Trained on 16 Graphcore Mk2 IPUs using [optimum-graphcore](https://github.com/huggingface/optimum-graphcore). |
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Command line: |
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``` |
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python examples/language-modeling/run_clm.py \ |
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--model_name_or_path gpt2-medium \ |
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--ipu_config_name Graphcore/gpt2-medium-ipu \ |
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--dataset_name wikitext \ |
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--dataset_config_name wikitext-103-raw-v1 \ |
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--do_train \ |
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--do_eval \ |
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--num_train_epochs 10 \ |
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--dataloader_num_workers 64 \ |
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--per_device_train_batch_size 1 \ |
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--per_device_eval_batch_size 1 \ |
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--gradient_accumulation_steps 256 \ |
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--output_dir /tmp/clm_output_medium \ |
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--logging_steps 5 \ |
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--learning_rate 1e-5 \ |
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--lr_scheduler_type linear \ |
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--loss_scaling 16384 \ |
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--weight_decay 0.01 \ |
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--warmup_ratio 0.1 \ |
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--ipu_config_overrides="embedding_serialization_factor=5,inference_device_iterations=9,replication_factor=2,inference_replication_factor=2,ipus_per_replica=8,layers_per_ipu=[0 3 3 3 3 4 4 4],matmul_proportion=0.25" \ |
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--dataloader_drop_last \ |
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--pod_type pod16 |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: IPU |
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- gradient_accumulation_steps: 256 |
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- total_train_batch_size: 1024 |
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- total_eval_batch_size: 18 |
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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_ratio: 0.1 |
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- num_epochs: 10.0 |
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- training precision: Mixed Precision |
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### Training results |
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``` |
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***** train metrics ***** |
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"epoch": 10.0, |
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"train_loss": 2.8070910754504506, |
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"train_runtime": 11217.8167, |
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"train_samples": 114248, |
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"train_samples_per_second": 101.845, |
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"train_steps_per_second": 0.099 |
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***** eval metrics ***** |
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"eval_loss": 2.697265625, |
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"eval_samples": 240, |
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"perplexity": 14.83910053420958 |
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``` |
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### Framework versions |
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- Transformers 4.18.0.dev0 |
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- Pytorch 1.10.0+cpu |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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