This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.

For more information look at:


    "dataset_name": "samsum",
    "do_eval": true,
    "do_train": true,
    "fp16": true,
    "learning_rate": 5e-05,
    "model_name_or_path": "sshleifer/distilbart-cnn-12-6",
    "num_train_epochs": 3,
    "output_dir": "/opt/ml/model",
    "per_device_eval_batch_size": 8,
    "per_device_train_batch_size": 8,
    "seed": 7

Train results

key value
epoch 3.0
init_mem_cpu_alloc_delta 180338
init_mem_cpu_peaked_delta 18282
init_mem_gpu_alloc_delta 1222242816
init_mem_gpu_peaked_delta 0
train_mem_cpu_alloc_delta 6971403
train_mem_cpu_peaked_delta 640733
train_mem_gpu_alloc_delta 4910897664
train_mem_gpu_peaked_delta 23331969536
train_runtime 155.2034
train_samples 14732
train_samples_per_second 2.242

Eval results

key value
epoch 3.0
eval_loss 1.4209576845169067
eval_mem_cpu_alloc_delta 868003
eval_mem_cpu_peaked_delta 18250
eval_mem_gpu_alloc_delta 0
eval_mem_gpu_peaked_delta 328244736
eval_runtime 0.6088
eval_samples 818
eval_samples_per_second 1343.647


from transformers import pipeline
summarizer = pipeline("summarization", model="philschmid/distilbart-cnn-12-6-samsum")

conversation = '''Jeff: Can I train a πŸ€— Transformers model on Amazon SageMaker? 
Philipp: Sure you can use the new Hugging Face Deep Learning Container. 
Jeff: ok.
Jeff: and how can I get started? 
Jeff: where can I find documentation? 
Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face                                           
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