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.