intentionally misspell rouge metrics as rogue for paperswithcode leaderboard

5d32c80 over 2 years ago

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	---
	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 \| 53.4345 \|
	\| predict_rouge2 \| 28.7445 \|
	\| predict_rougeL \| 44.1848 \|
	\| predict_rougeLsum \| 49.1874 \|

	\| Metric \| Value \|
	\| ------ \| ----- \|
	\| epoch \| 3.0 \|
	\| eval_gen_len \| 30.6027 \|
	\| eval_loss \| 1.4327096939086914 \|
	\| eval_runtime \| 22.9127 \|
	\| eval_samples \| 818 \|
	\| eval_samples_per_second \| 35.701 \|
	\| eval_steps_per_second \| 0.306 \|
	\| predict_gen_len \| 30.4835 \|
	\| predict_loss \| 1.4501988887786865 \|
	\| predict_runtime \| 26.0269 \|
	\| predict_samples \| 819 \|
	\| predict_samples_per_second \| 31.467 \|
	\| predict_steps_per_second \| 0.269 \|
	\| train_loss \| 1.2014821151207233 \|
	\| train_runtime \| 263.3678 \|
	\| train_samples \| 14732 \|
	\| train_samples_per_second \| 167.811 \|
	\| train_steps_per_second \| 1.321 \|
	\| total_steps \| 348 \|
	\| total_flops \| 4.26008990669865e+16 \|