{
    "dataset_name": "samsum",
    "do_eval": true,
    "do_train": true,
    "fp16": true,
    "learning_rate": 5e-05,
    "model_name_or_path": "facebook/bart-base",
    "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
init_mem_cpu_alloc_delta	180190
init_mem_cpu_peaked_delta	18282
init_mem_gpu_alloc_delta	558658048
init_mem_gpu_peaked_delta	0
train_mem_cpu_alloc_delta	6658519
train_mem_cpu_peaked_delta	642937
train_mem_gpu_alloc_delta	2267624448
train_mem_gpu_peaked_delta	10355728896
train_runtime	98.4931
train_samples	14732
train_samples_per_second	3.533

Eval results

key	value
epoch	3
eval_loss	1.5356481075286865
eval_mem_cpu_alloc_delta	659047
eval_mem_cpu_peaked_delta	18254
eval_mem_gpu_alloc_delta	0
eval_mem_gpu_peaked_delta	300285440
eval_runtime	0.3116
eval_samples	818
eval_samples_per_second	2625.337

Usage

python

from transformers import pipeline
summarizer = pipeline("summarization", model="philschmid/bart-base-samsum")

conversation = '''Jeff: Can I train a 🤗 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                                           
'''
nlp(conversation)