readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - summarization
  - azureml
  - azure
  - codecarbon
  - bart
  datasets:
  - samsum
  metrics:
  - rouge
  model-index:
  - name: bart-large-samsum
    results:
    - task: 
        name: Abstractive Text Summarization
        type: abstractive-text-summarization
      dataset:
        name: "SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization" 
        type: samsum
      metrics:
      - name: Validation ROGUE-1
        type: rouge-1
        value: 55.0234
      - name: Validation ROGUE-2
        type: rouge-2
        value: 29.6005
      - name: Validation ROGUE-L
        type: rouge-L
        value: 44.914
      - name: Validation ROGUE-Lsum
        type: rouge-Lsum
        value: 50.464
      - name: Test ROGUE-1
        type: rouge-1
        value: 53.4345
      - name: Test ROGUE-2
        type: rouge-2
        value: 28.7445
      - name: Test ROGUE-L
        type: rouge-L
        value: 44.1848
      - name: Test ROGUE-Lsum
        type: rouge-Lsum
        value: 49.1874
  widget:
  - text: | 
      Henry: Hey, is Nate coming over to watch the movie tonight?
      Kevin: Yea, he said he'll be arriving a bit later at around 7 since he gets off of work at 6. Have you taken out the garbage yet?
      Henry: Oh I forgot. I'll do that once I'm finished with my assignment for my math class.
      Kevin: Yea, you should take it out as soon as possible. And also, Nate is bringing his girlfriend.
      Henry: Nice, I'm really looking forward to seeing them again.
  ---
  
  ## `bart-large-samsum`
  This model was trained using Microsoft's [`Azure Machine Learning Service`](https://azure.microsoft.com/en-us/services/machine-learning). It was fine-tuned on the [`samsum`](https://huggingface.co/datasets/samsum) corpus from [`facebook/bart-large`](https://huggingface.co/facebook/bart-large) checkpoint.
  
  ## Usage (Inference)
  ```python
  from transformers import pipeline
  summarizer = pipeline("summarization", model="linydub/bart-large-samsum")
  
  input_text = '''
      Henry: Hey, is Nate coming over to watch the movie tonight?
      Kevin: Yea, he said he'll be arriving a bit later at around 7 since he gets off of work at 6. Have you taken out the garbage yet?
      Henry: Oh I forgot. I'll do that once I'm finished with my assignment for my math class.
      Kevin: Yea, you should take it out as soon as possible. And also, Nate is bringing his girlfriend.
      Henry: Nice, I'm really looking forward to seeing them again.
  '''
  summarizer(input_text)
  ```
  
  ## Fine-tune on AzureML
  [![Deploy to Azure](https://aka.ms/deploytoazurebutton)](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2Flinydub%2Fazureml-greenai-txtsum%2Fmain%2F.cloud%2Ftemplate-hub%2Flinydub%2Farm-bart-large-samsum.json) [![Visualize](https://raw.githubusercontent.com/Azure/azure-quickstart-templates/master/1-CONTRIBUTION-GUIDE/images/visualizebutton.svg?sanitize=true)](http://armviz.io/#/?load=https://raw.githubusercontent.com/linydub/azureml-greenai-txtsum/main/.cloud/template-hub/linydub/arm-bart-large-samsum.json)
  
  More information about the fine-tuning process (including samples and benchmarks):  
  **[Preview]** https://github.com/linydub/azureml-greenai-txtsum
  
  ## Resource Usage
  These results were retrieved from [`Azure Monitor Metrics`](https://docs.microsoft.com/en-us/azure/azure-monitor/essentials/data-platform-metrics). All experiments were ran on AzureML low priority compute clusters.
  
  | Key | Value |
  | --- | ----- |
  | Region | US West 2 |
  | AzureML Compute SKU | STANDARD_ND40RS_V2 |
  | Compute SKU GPU Device | 8 x NVIDIA V100 32GB (NVLink) |
  | Compute Node Count | 1 |
  | Run Duration | 6m 48s |
  | Compute Cost (Dedicated/LowPriority) | $2.50 / $0.50 USD |
  | Average CPU Utilization | 47.9% |
  | Average GPU Utilization | 69.8% |
  | Average GPU Memory Usage | 25.71 GB |
  | Total GPU Energy Usage | 370.84 kJ |
  
  
  *Compute cost ($) is estimated from the run duration, number of compute nodes utilized, and SKU's price per hour. Updated SKU pricing could be found [here](https://azure.microsoft.com/en-us/pricing/details/machine-learning).  
  
  ### Carbon Emissions
  These results were obtained using [`CodeCarbon`](https://github.com/mlco2/codecarbon). The carbon emissions are estimated from training runtime only (excl. setup and evaluation runtimes).  
  
  | Key | Value |
  | --- | ----- |
  | timestamp | 2021-09-16T23:54:25 |
  | duration | 263.2430217266083 |
  | emissions | 0.029715544634717518 |
  | energy_consumed | 0.09985062041235725 |
  | country_name | USA |
  | region | Washington |
  | cloud_provider | azure |
  | cloud_region | westus2 |
  
  ## Hyperparameters
  
  - max_source_length: 512
  - max_target_length: 90
  - fp16: True
  - seed: 1
  - per_device_train_batch_size: 16
  - per_device_eval_batch_size: 16
  - gradient_accumulation_steps: 1
  - learning_rate: 5e-5
  - num_train_epochs: 3.0
  - weight_decay: 0.1
  
  
  
  ## Results
  | ROUGE | Score |
  | ----- | ----- |
  | eval_rouge1 | 55.0234 |
  | eval_rouge2 | 29.6005 |
  | eval_rougeL | 44.914 |
  | eval_rougeLsum | 50.464 |
  | predict_rouge1 | 53