story2dialogue-SODA-BART-Large-LoRA

abirmondalind/story2dialogue-SODA-BART-Large-LoRA

story2dialogue-SODA-BART-Large-LoRA is a machine learning model from abirmondalind. 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 peft. The card lists the license as apache-2.0.

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

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Hugging Face

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At a glance

Library: peft
License: apache-2.0
Access: Public
Created: Jan 21, 2026
Updated: Jan 27, 2026
SHA: 8d283d9e

Base models

facebook/bart-large

Model card

From the Hugging Face model README

story2dialogue-SODA-BART-Large-LoRA

This model is a fine-tuned version of facebook/bart-large on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.3667
Rouge1: 0.2848
Rouge2: 0.1241
Rougel: 0.2545
Rougelsum: 0.2543
Bleu: 0.0720
Bleu1: 0.3624
Bleu2: 0.1387
Bleu3: 0.0789
Bleu4: 0.0503
Meteor: 0.2572
Avg Distinct 1: 0.9633
Avg Distinct 2: 0.9975
Avg Distinct 3: 0.9930
Avg Jaccard: 0.1273
Gen Length: 10.7279

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Bleu	Bleu1	Bleu2	Bleu3	Bleu4	Meteor	Avg Distinct 1	Avg Distinct 2	Avg Distinct 3	Avg Jaccard	Gen Length
1.7823	0.2004	1000	1.5746	0.2044	0.0646	0.1778	0.1776	0.0345	0.2822	0.0726	0.0343	0.0181	0.1810	0.9572	0.9978	0.9991	0.0760	10.4520
1.7107	0.4009	2000	1.5063	0.2327	0.0818	0.2037	0.2035	0.0461	0.3092	0.0926	0.0456	0.0248	0.2068	0.9526	0.9975	0.9997	0.0937	10.8824
1.6482	0.6013	3000	1.4627	0.2427	0.0899	0.2127	0.2127	0.0512	0.3183	0.1007	0.0513	0.0294	0.2168	0.9543	0.9967	0.9995	0.0997	10.9731
1.6283	0.8018	4000	1.4502	0.2498	0.0928	0.2192	0.2191	0.0535	0.3297	0.1065	0.0552	0.0325	0.2238	0.9642	0.9977	0.9990	0.1026	10.6925
1.6046	1.0022	5000	1.4270	0.2548	0.1001	0.2250	0.2251	0.0580	0.3366	0.1125	0.0599	0.0355	0.2324	0.9587	0.9979	0.9987	0.1079	10.8118
1.5787	1.2026	6000	1.4217	0.2592	0.1029	0.2304	0.2302	0.0613	0.3428	0.1180	0.0640	0.0389	0.2353	0.9664	0.9980	0.9977	0.1099	10.7235
1.5661	1.4031	7000	1.4012	0.2724	0.1124	0.2425	0.2423	0.0648	0.3513	0.1251	0.0686	0.0418	0.2476	0.9637	0.9977	0.9965	0.1171	10.7619
1.5572	1.6035	8000	1.3973	0.2738	0.1133	0.2428	0.2426	0.0668	0.3476	0.1269	0.0697	0.0426	0.2463	0.9553	0.9968	0.9963	0.1181	11.0247
1.5365	1.8040	9000	1.3891	0.2787	0.1192	0.2488	0.2487	0.0684	0.3534	0.1326	0.0740	0.0462	0.2507	0.9590	0.9972	0.9932	0.1222	10.8450
1.5298	2.0044	10000	1.3805	0.2767	0.1180	0.2477	0.2476	0.0678	0.3587	0.1336	0.0745	0.0464	0.2510	0.9628	0.9976	0.9935	0.1217	10.5836
1.5201	2.2049	11000	1.3786	0.2823	0.1213	0.2524	0.2522	0.0708	0.3578	0.1350	0.0755	0.0474	0.2557	0.9614	0.9975	0.9953	0.1246	10.8934
1.5322	2.4053	12000	1.3780	0.2842	0.1235	0.2538	0.2536	0.0705	0.3597	0.1372	0.0774	0.0490	0.2553	0.9604	0.9974	0.9940	0.1256	10.7393
1.5279	2.6057	13000	1.3695	0.2834	0.1228	0.2533	0.2532	0.0715	0.3591	0.1364	0.0779	0.0494	0.2564	0.9656	0.9980	0.9937	0.1259	10.8033
1.5146	2.8062	14000	1.3667	0.2848	0.1241	0.2545	0.2543	0.0720	0.3624	0.1387	0.0789	0.0503	0.2572	0.9633	0.9975	0.9930	0.1273	10.7279

Framework versions

PEFT 0.15.2
Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.2

Library: peft
License: apache-2.0
Created: Jan 21, 2026
Updated: Jan 27, 2026