bart-large-cnn-samsum-rescom Open-source Text Summarization Model - Generate High-quality Resume Summaries for Free

Bart Large Cnn Samsum Rescom Finetuned Resume Summarizer 10 Epoch Tweak Lr 8 100 1

Developed by Ameer05

A text summarization model based on the BART architecture, fine-tuned on an unknown dataset, excels at generating resume summaries

Text Generation

Transformers

#Resume Summary Generation #High ROUGE Score #BART Fine-tuning

Downloads 18

Release Time : 3/8/2022

Model Overview

This model is a fine-tuned text summarization model based on BART-large-cnn architecture, specifically optimized for resume summarization tasks

Model Features

High-Quality Summary Generation

Achieved a Rouge1 score of 61.441 on the evaluation set, indicating high-quality summarization

Resume Summary Optimization

Specifically fine-tuned for resume content, suitable for generating career-related summaries

Efficient Training Configuration

Utilized techniques like gradient accumulation and mixed-precision training to optimize training efficiency

Model Capabilities

Text Summarization Generation

Resume Content Refinement

Key Information Extraction

Use Cases

Career Services

Automatic Resume Summarization

Generates concise and professional summaries for job seekers' resumes

Rouge1 score of 61.441, indicating good summarization quality

Human Resources

Candidate Resume Screening

Helps HR quickly browse key information from large volumes of resumes

🚀 bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch-tweak-lr-8-100-1

This model is a fine - tuned version of Ameer05/model-token-repo on an unknown dataset. It can be used for summarization tasks and has achieved certain results on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of Ameer05/model-token-repo on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.6315
Rouge1: 61.441
Rouge2: 52.9403
Rougel: 58.3426
Rougelsum: 60.8249

📚 Documentation

Training and evaluation data

More information needed

Model description

More information needed

Intended uses & limitations

More information needed

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e - 05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
No log	0.91	5	2.0139	53.4301	46.6698	50.644	53.3985
No log	1.91	10	1.6309	61.4629	46.6698	50.644	53.3985
No log	2.91	15	1.5379	61.2938	53.7208	59.0644	60.7381
No log	3.91	20	1.4470	63.2667	55.9273	60.5112	62.7538
1.5454	4.91	25	1.4353	62.7166	54.8328	60.0101	62.1378
1.5454	5.91	30	1.4411	59.7469	51.9068	57.036	58.9474
1.5454	6.91	35	1.5195	64.152	57.1447	61.362	63.5951
1.5454	7.91	40	1.6174	60.1464	51.5654	57.1676	59.4405
0.5429	8.91	45	1.7451	61.9696	53.6421	58.5884	61.3286
0.5429	9.91	50	1.9081	60.3296	52.3052	57.6518	59.7854
0.5429	10.91	55	1.9721	61.5597	51.9027	57.1184	60.6717
0.5429	11.91	60	2.0471	61.2222	53.9475	58.725	60.6668
0.5429	12.91	65	2.1422	60.1915	52.0627	56.9955	59.438
0.1506	13.91	70	2.1542	61.6915	53.045	58.1727	60.8765
0.1506	14.91	75	2.1885	59.8069	51.6543	56.8112	59.2055
0.1506	15.91	80	2.3146	61.695	53.2666	57.9003	61.1108
0.1506	16.91	85	2.3147	60.4482	52.1694	57.0649	59.7882
0.0452	17.91	90	2.1731	60.0259	51.5046	56.7399	59.2955
0.0452	18.91	95	2.2690	60.0534	52.4819	57.1631	59.5056
0.0452	19.91	100	2.2990	58.0737	48.8098	54.5684	57.3187
0.0452	20.91	105	2.2704	61.8982	53.9077	58.6909	61.4252
0.0267	21.91	110	2.3012	62.0174	53.5427	58.5278	61.1921
0.0267	22.91	115	2.3569	61.6327	53.7387	58.8908	61.1623
0.0267	23.91	120	2.3579	60.228	52.3747	58.1448	59.7322
0.0267	24.91	125	2.3389	60.4902	51.7935	57.0689	59.7132
0.0267	25.91	130	2.3168	58.8469	50.3181	55.7386	58.3598
0.0211	26.91	135	2.4147	59.4225	50.8405	56.503	58.7221
0.0211	27.91	140	2.3631	59.7489	51.2137	57.3204	59.3348
0.0211	28.91	145	2.3850	60.1718	51.4176	57.2152	59.5157
0.0211	29.91	150	2.4610	60.1433	51.433	56.6256	59.3265
0.0175	30.91	155	2.4400	58.8345	49.7031	55.3079	57.9236
0.0175	31.91	160	2.4506	59.209	50.1626	55.6451	58.5791
0.0175	32.91	165	2.4316	59.7713	50.8999	56.4235	58.9845
0.0175	33.91	170	2.2781	60.1822	51.9435	57.4586	59.6766
0.0175	34.91	175	2.3849	58.2328	49.2106	55.1516	57.5072
0.0141	35.91	180	2.4872	58.4916	50.3345	55.5991	58.1131
0.0141	36.91	185	2.4883	59.0957	49.76	55.3567	58.076
0.0141	37.91	190	2.4327	58.091	48.8628	54.8678	57.5406
0.0141	38.91	195	2.4998	57.7428	48.7366	54.2166	56.7643
0.0089	39.91	200	2.4107	60.1662	51.9832	57.1372	59.6989
0.0089	40.91	205	2.4700	58.2159	49.3934	54.9265	57.4126
0.0089	41.91	210	2.4833	58.7434	49.6619	55.5239	57.9562
0.0089	42.91	215	2.4703	60.2984	51.3168	56.9082	59.3958
0.0062	43.91	220	2.5306	60.5455	52.1189	57.3213	60.0232
0.0062	44.91	225	2.5181	60.2149	51.2187	56.1935	59.3471
0.0062	45.91	230	2.4871	59.8013	51.6114	56.0911	59.0902
0.0062	46.91	235	2.4811	58.0271	48.9441	54.3108	57.3647
0.0062	47.91	240	2.5290	62.5087	54.6149	59.638	62.0455
0.0072	48.91	245	2.5194	58.7193	49.9679	55.6517	58.1569
0.0072	49.91	250	2.5708	58.4626	49.5257	54.5032	58.1413
0.0072	50.91	255	2.6449	58.446	49.4625	55.1092	58.03
0.0072	51.91	260	2.5592	58.859	49.4398	55.1503	57.9663
0.0056	52.91	265	2.5086	59.7322	51.3051	56.5401	59.2726
0.0056	53.91	270	2.4846	57.8603	48.2408	54.3847	57.115
0.0056	54.91	275	2.5509	58.9506	50.045	55.6658	58.3618
0.0056	55.91	280	2.5032	60.2524	51.8167	56.98	59.7506
0.0056	56.91	285	2.5012	60.0596	51.4924	56.7181	59.5037
0.0054	57.91	290	2.5176	61.0622	52.6235	57.9317	60.5036
0.0054	58.91	295	2.5024	62.9246	54.8544	59.9824	62.5584
0.0054	59.91	300	2.5687	62.2602	53.9673	58.9862	61.5837
0.0054	60.91	305	2.5890	62.5706	54.227	59.2032	62.125
0.0036	61.91	310	2.5454	62.1565	53.2585	58.7169	61.3943
0.0036	62.91	315	2.5629	62.8292	54.6781	59.9889	62.254
0.0036	63.91	320	2.5581	58.8394	50.4421	56.0742	58.1945
0.0036	64.91	325	2.5532	59.5814	51.1335	56.5841	59.196
0.0031	65.91	330	2.5826	59.0485	50.3992	55.5283	58.3757
0.0031	66.91	335	2.5815	61.4832	52.7977	57.7351	60.9888
0.0031	67.91	340	2.5865	61.7836	53.6797	58.6743	61.3765
0.0031	68.91	345	2.6007	61.2253	52.8781	57.7006	60.717
0.0031	69.91	350	2.6210	60.717	52.4933	57.5089	60.4196
0.0035	70.91	355	2.6169	61.3491	53.3932	58.2288	60.8793
0.0035	71.91	360	2.6025	62.0101	54.0289	59.0822	61.7202
0.0035	72.91	365	2.5705	61.2227	52.9937	58.2493	60.6631
0.0035	73.91	370	2.5623	59.1718	50.7827	56.1851	58.7118
0.002	74.91	375	2.5536	58.4201	49.6923	55.0398	57.7707
0.002	75.91	380	2.5478	60.2307	51.7503	57.3173	59.692
0.002	76.91	385	2.6039	58.7637	49.741	55.5341	58.0784
0.002	77.91	390	2.6371	59.3929	50.6444	55.9887	58.813
0.002	78.91	395	2.6238	59.0572	50.605	55.6631	58.4366
0.0019	79.91	400	2.5783	57.9852	49.2588	54.822	57.4643
0.0019	80.91	405	2.5982	58.0218	49.1651	54.9876	57.4066
0.0019	81.91	410	2.6141	60.3133	51.5723	56.9476	59.715
0.0019	82.91	415	2.5904	60.8199	51.8956	58.406	60.323
0.0017	83.91	420	2.5718	60.3449	51.1433	57.6984	59.7513
0.0017	84.91	425	2.5737	60.151	51.1986	57.3376	59.378
0.0017	85.91	430	2.5807	60.9273	52.2469	58.2038	60.1642
0.0017	86.91	435	2.5900	60.1846	51.6144	57.5407	59.5109
0.0011	87.91	440	2.6066	62.0776	53.6022	59.157	61.6201
0.0011	88.91	445	2.6231	61.8822	53.5232	58.965	61.401
0.0011	89.91	450	2.6273	60.3358	51.9941	57.3823	59.7729
0.0011	90.91	455	2.6194	60.0196	51.6134	57.1357	59.4594
0.0011	91.91	460	2.6118	60.6898	52.1328	57.3076	60.0351
0.0015	92.91	465	2.6032	61.2119	52.5034	57.8098	60.6634
0.0015	93.91	470	2.6040	61.4812	52.8197	57.9668	60.8767
0.0015	94.91	475	2.6158	61.4046	52.8905	57.8958	60.804
0.0015	95.91	480	2.6280	62.1764	53.8521	58.8608	61.6138
0.0012	96.91	485	2.6304	62.2028	53.8967	58.8976	61.6409
0.0012	97.91	490	2.6328	61.7371	53.3908	58.4107	61.1382
0.0012	98.91	495	2.6331	61.441	52.9403	58.3426	60.8249
0.0012	99.91	500	2.6315	61.441	52.9403	58.3426	60.8249

Framework versions

Transformers 4.15.0
Pytorch 1.9.1
Datasets 1.18.4
Tokenizers 0.10.3

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