text_shortening_model_v44

ldos/text_shortening_model_v44

text_shortening_model_v44 is a machine learning model from ldos. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: mit
Model type: bart
Access: Public
Created: Sep 20, 2023
Updated: Sep 20, 2023
SHA: dad7a2f8

Base models

facebook/bart-large-xsum

Model card

From the Hugging Face model README

text_shortening_model_v44

This model is a fine-tuned version of facebook/bart-large-xsum on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.8836
Rouge1: 0.4921
Rouge2: 0.2719
Rougel: 0.4429
Rougelsum: 0.4423
Bert precision: 0.8746
Bert recall: 0.8761
Average word count: 8.7063
Max word count: 17
Min word count: 5
Average token count: 16.2989
% shortened texts with length > 12: 8.7302

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Bert precision	Bert recall	Average word count	Max word count	Min word count	Average token count	% shortened texts with length > 12
1.0083	1.0	83	1.4717	0.4904	0.2378	0.426	0.4266	0.8725	0.8732	8.5794	18	4	15.6164	6.3492
0.5702	2.0	166	1.4852	0.4722	0.2421	0.414	0.4143	0.869	0.8653	7.9101	14	4	13.6455	1.5873
0.4588	3.0	249	1.6283	0.5038	0.2733	0.4424	0.4422	0.8732	0.8794	9.0053	16	4	16.8386	8.9947
0.3586	4.0	332	1.6017	0.4965	0.2762	0.4381	0.4383	0.8709	0.8787	9.2381	18	4	16.3042	12.1693
0.2479	5.0	415	1.7497	0.4794	0.2613	0.4295	0.43	0.872	0.8702	8.3228	15	4	15.209	3.1746
0.2296	6.0	498	1.8482	0.4935	0.2739	0.4442	0.4443	0.8737	0.8755	8.7963	17	5	16.2989	7.1429
0.3065	7.0	581	1.9485	0.4765	0.2552	0.4213	0.4212	0.8698	0.8693	8.4683	17	5	15.6005	7.9365
0.2598	8.0	664	2.1608	0.4871	0.2585	0.4316	0.4319	0.8707	0.8736	8.963	16	5	16.6481	9.5238
0.2707	9.0	747	2.0966	0.4758	0.2603	0.4231	0.4246	0.8709	0.8717	8.4841	16	4	15.9312	7.1429
0.2099	10.0	830	2.2721	0.4777	0.2604	0.4246	0.4246	0.8735	0.8724	8.4312	15	4	15.9471	5.5556
0.1668	11.0	913	2.3536	0.4758	0.2541	0.4331	0.4328	0.8721	0.87	8.2857	14	4	15.7725	3.1746
0.1552	12.0	996	2.4572	0.484	0.2562	0.4313	0.4304	0.8726	0.875	8.828	17	4	16.246	7.9365
0.2141	13.0	1079	2.4485	0.4785	0.2631	0.4257	0.4252	0.8678	0.8736	9.1402	19	4	16.6561	11.3757
0.1348	14.0	1162	2.5012	0.4821	0.2613	0.4292	0.4296	0.8706	0.8738	8.8783	17	4	16.5185	10.0529
0.074	15.0	1245	2.5309	0.4915	0.2745	0.445	0.444	0.8764	0.8768	8.6667	16	4	16.2513	9.2593
0.1822	16.0	1328	2.5735	0.4709	0.2566	0.4239	0.4232	0.872	0.8692	8.2063	15	3	15.7249	4.2328
0.086	17.0	1411	2.8597	0.4831	0.2675	0.4373	0.4372	0.8722	0.8743	8.754	16	5	16.5476	8.7302
0.0872	18.0	1494	2.7420	0.4831	0.2677	0.4367	0.4353	0.8724	0.873	8.664	17	5	16.3016	7.672
0.1164	19.0	1577	2.8790	0.4867	0.269	0.4388	0.4381	0.8737	0.8755	8.7725	17	5	16.4418	8.9947
0.1101	20.0	1660	2.8836	0.4921	0.2719	0.4429	0.4423	0.8746	0.8761	8.7063	17	5	16.2989	8.7302

Framework versions

Transformers 4.33.1
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

Library: transformers
Type: bart
License: mit
Created: Sep 20, 2023
Updated: Sep 20, 2023