philschmid HF staff commited on
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README.md ADDED
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+
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+ ---
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+ language: en
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+ tags:
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+ - sagemaker
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+ - bart
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+ - summarization
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+ license: apache-2.0
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+ datasets:
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+ - samsum
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+ widget:
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+ - text: |
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+ Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker?
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+ Philipp: Sure you can use the new Hugging Face Deep Learning Container.
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+ Jeff: ok.
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+ Jeff: and how can I get started?
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+ Jeff: where can I find documentation?
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+ Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face
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+ ---
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+
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+ ## `distilbart-cnn-12-6-samsum`
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+
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+ This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
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+
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+ For more information look at:
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+ - [🤗 Transformers Documentation: Amazon SageMaker](https://huggingface.co/transformers/sagemaker.html)
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+ - [Example Notebooks](https://github.com/huggingface/notebooks/tree/master/sagemaker)
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+ - [Amazon SageMaker documentation for Hugging Face](https://docs.aws.amazon.com/sagemaker/latest/dg/hugging-face.html)
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+ - [Python SDK SageMaker documentation for Hugging Face](https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/index.html)
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+ - [Deep Learning Container](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-training-containers)
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+
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+ ## Result
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+
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+ ### Hyperparameters
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+ ```json
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+ {
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+ "dataset_name": "samsum",
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+ "do_eval": true,
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+ "do_train": true,
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+ "fp16": true,
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+ "learning_rate": 5e-05,
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+ "model_name_or_path": "sshleifer/distilbart-cnn-12-6",
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+ "num_train_epochs": 3,
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+ "output_dir": "/opt/ml/model",
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+ "per_device_eval_batch_size": 8,
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+ "per_device_train_batch_size": 8,
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+ "seed": 7
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+ }
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+ ```
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+
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+ ### Training
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+
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+ | key | value |
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+ | --- | ----- |
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+ | epoch | 3.0 |
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+ | init_mem_cpu_alloc_delta | 180338 |
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+ | init_mem_cpu_peaked_delta | 18282 |
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+ | init_mem_gpu_alloc_delta | 1222242816 |
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+ | init_mem_gpu_peaked_delta | 0 |
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+ | train_mem_cpu_alloc_delta | 6971403 |
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+ | train_mem_cpu_peaked_delta | 640733 |
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+ | train_mem_gpu_alloc_delta | 4910897664 |
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+ | train_mem_gpu_peaked_delta | 23331969536 |
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+ | train_runtime | 155.2034 |
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+ | train_samples | 14732 |
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+ | train_samples_per_second | 2.242 |
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+
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+ ### Evaluation
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+
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+ | key | value |
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+ | --- | ----- |
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+ | epoch | 3.0 |
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+ | eval_loss | 1.4209576845169067 |
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+ | eval_mem_cpu_alloc_delta | 868003 |
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+ | eval_mem_cpu_peaked_delta | 18250 |
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+ | eval_mem_gpu_alloc_delta | 0 |
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+ | eval_mem_gpu_peaked_delta | 328244736 |
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+ | eval_runtime | 0.6088 |
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+ | eval_samples | 818 |
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+ | eval_samples_per_second | 1343.647 |
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+
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+
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+ ## Usage
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+ ```python
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+ from transformers import pipeline
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+ summarizer = pipeline("summarization", model="philschmid/distilbart-cnn-12-6-samsum")
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+
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+ conversation = '''Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker?
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+ Philipp: Sure you can use the new Hugging Face Deep Learning Container.
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+ Jeff: ok.
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+ Jeff: and how can I get started?
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+ Jeff: where can I find documentation?
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+ Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face
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+ '''
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+ nlp(conversation)
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+ ```
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+ "init_mem_gpu_alloc_delta": 1222242816,
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+ "init_mem_gpu_peaked_delta": 0,
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+ "train_mem_cpu_alloc_delta": 6971403,
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+ "train_runtime": 155.2034,
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+ "train_samples": 14732,
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+ "train_samples_per_second": 2.242
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+ }
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+ {
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+ "_name_or_path": "sshleifer/distilbart-cnn-12-6",
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+ "activation_dropout": 0.0,
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+ "activation_function": "gelu",
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+ "architectures": [
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+ "decoder_layers": 6,
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+ "dropout": 0.1,
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+ "early_stopping": true,
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+ "encoder_layers": 12,
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+ "gradient_checkpointing": false,
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+ "id2label": {
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+ "1": "LABEL_1",
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+ "2": "LABEL_2"
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+ "init_std": 0.02,
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+ "is_encoder_decoder": true,
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+ "LABEL_0": 0,
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+ "length_penalty": 2.0,
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+ "max_position_embeddings": 1024,
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+ "min_length": 56,
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+ "model_type": "bart",
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+ "no_repeat_ngram_size": 3,
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+ "normalize_before": false,
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+ "normalize_embedding": true,
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+ "num_beams": 4,
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+ "num_hidden_layers": 12,
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+ "output_past": true,
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+ "scale_embedding": false,
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+ "student_decoder_layers": null,
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+ "task_specific_params": {
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+ "summarization": {
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+ "early_stopping": true,
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+ }
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+ },
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+ "transformers_version": "4.4.2",
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+ "use_cache": true,
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+ "vocab_size": 50264
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+ }
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