đ bart-large-samsum
This bart-large-samsum model is designed for abstractive text summarization, leveraging Microsoft's Azure Machine Learning Service and fine - tuned on the samsum corpus. It offers high - quality summarization results with detailed performance metrics.
đ Quick Start
This model was trained using Microsoft's Azure Machine Learning Service. It was fine - tuned on the samsum corpus from facebook/bart-large checkpoint.
đģ Usage Examples
Basic Usage
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)
đ Documentation
Fine - tune on AzureML
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. All experiments were ran on AzureML low priority compute clusters.
| Property |
Details |
| 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.
Carbon Emissions
These results were obtained using CodeCarbon. The carbon emissions are estimated from training runtime only (excl. setup and evaluation runtimes).
| Property |
Details |
| 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 |
đ License
This project is licensed under the Apache - 2.0 license.
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