Update README.md

README.md

@@ -132,4 +132,79 @@ model-index:
  name: gen_len
  verified: true
  verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTdhNGYxYWRlNTU0MzAxMWU1NzNmMTBjMmY3NzkzODAyYTMzZWYyZmNiMTViMzNmYTE0ZmFmNDdhMzQwMmJkNyIsInZlcnNpb24iOjF9.8lm84JtbCh-diuNQ01oXK6P8vV9CPyA8y-7D9o_OHb9Vk3pNEFM1jMSZVdEG9wFuMpWL3ARbXLadEPQB5HN8AQ
----
+---
+
+## `distilbart-cnn-12-6-samsum`
+
+This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
+
+For more information look at:
+- [🤗 Transformers Documentation: Amazon SageMaker](https://huggingface.co/transformers/sagemaker.html)
+- [Example Notebooks](https://github.com/huggingface/notebooks/tree/master/sagemaker)
+- [Amazon SageMaker documentation for Hugging Face](https://docs.aws.amazon.com/sagemaker/latest/dg/hugging-face.html)
+- [Python SDK SageMaker documentation for Hugging Face](https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/index.html)
+- [Deep Learning Container](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-training-containers)
+
+## Hyperparameters
+```json
+{
+ "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 |
+
+
+## Usage
+```python
+from transformers import pipeline
+summarizer = pipeline("summarization", model="philschmid/distilbart-cnn-12-6-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)
+```