bart-large-cnn-finetuned-promt_generation

satyanshu404/bart-large-cnn-finetuned-promt_generation

bart-large-cnn-finetuned-promt_generation is a machine learning model from satyanshu404. 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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Hugging Face

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At a glance

Library: transformers
License: mit
Model type: bart
Access: Public
Created: Oct 11, 2023
Updated: Oct 11, 2023
SHA: 8225517c

Base models

facebook/bart-large-cnn

Model card

From the Hugging Face model README

bart-large-cnn-finetuned-promt_generation

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

Loss: 1.8767
Map: 0.3718
Ndcg@10: 0.5915

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: 3e-07
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Map	Ndcg@10
No log	1.0	4	3.3856	0.2563	0.4531
No log	2.0	8	3.3740	0.2563	0.4531
No log	3.0	12	3.3430	0.2563	0.4531
No log	4.0	16	3.2912	0.2563	0.4531
No log	5.0	20	3.2468	0.2563	0.4531
No log	6.0	24	3.2199	0.2563	0.4531
No log	7.0	28	3.2016	0.2563	0.4531
No log	8.0	32	3.0741	0.2563	0.4531
No log	9.0	36	3.0260	0.2563	0.4531
No log	10.0	40	2.9989	0.2563	0.4531
No log	11.0	44	2.9755	0.2563	0.4531
No log	12.0	48	2.9495	0.2560	0.4528
No log	13.0	52	2.9300	0.2560	0.4528
No log	14.0	56	2.9088	0.2560	0.4528
No log	15.0	60	2.8656	0.2560	0.4528
No log	16.0	64	2.8146	0.2560	0.4528
No log	17.0	68	2.7699	0.2560	0.4528
No log	18.0	72	2.7321	0.2577	0.4542
No log	19.0	76	2.6978	0.2577	0.4542
No log	20.0	80	2.6665	0.2577	0.4542
No log	21.0	84	2.6373	0.2577	0.4542
No log	22.0	88	2.6080	0.2897	0.4974
No log	23.0	92	2.5812	0.2897	0.4974
No log	24.0	96	2.5568	0.2954	0.5014
No log	25.0	100	2.5348	0.2954	0.5014
No log	26.0	104	2.5133	0.2954	0.5014
No log	27.0	108	2.4929	0.2954	0.5014
No log	28.0	112	2.4735	0.3385	0.5472
No log	29.0	116	2.4553	0.3385	0.5472
No log	30.0	120	2.4374	0.3385	0.5472
No log	31.0	124	2.4201	0.3385	0.5472
No log	32.0	128	2.4035	0.3385	0.5472
No log	33.0	132	2.3870	0.3385	0.5472
No log	34.0	136	2.3711	0.3385	0.5472
No log	35.0	140	2.3556	0.3385	0.5472
No log	36.0	144	2.3397	0.3385	0.5472
No log	37.0	148	2.3246	0.3385	0.5472
No log	38.0	152	2.3097	0.3385	0.5472
No log	39.0	156	2.2944	0.3718	0.5915
No log	40.0	160	2.2801	0.3718	0.5915
No log	41.0	164	2.2660	0.3718	0.5915
No log	42.0	168	2.2525	0.3718	0.5915
No log	43.0	172	2.2392	0.3718	0.5915
No log	44.0	176	2.2267	0.3718	0.5915
No log	45.0	180	2.2135	0.3718	0.5915
No log	46.0	184	2.2007	0.3718	0.5915
No log	47.0	188	2.1875	0.3718	0.5915
No log	48.0	192	2.1752	0.3718	0.5915
No log	49.0	196	2.1637	0.3718	0.5915
No log	50.0	200	2.1514	0.3718	0.5915
No log	51.0	204	2.1393	0.3718	0.5915
No log	52.0	208	2.1281	0.3718	0.5915
No log	53.0	212	2.1159	0.3718	0.5915
No log	54.0	216	2.1048	0.3718	0.5915
No log	55.0	220	2.0941	0.3718	0.5915
No log	56.0	224	2.0829	0.3718	0.5915
No log	57.0	228	2.0727	0.3718	0.5915
No log	58.0	232	2.0617	0.3718	0.5915
No log	59.0	236	2.0518	0.3718	0.5915
No log	60.0	240	2.0416	0.3718	0.5915
No log	61.0	244	2.0323	0.3718	0.5915
No log	62.0	248	2.0230	0.3718	0.5915
No log	63.0	252	2.0143	0.3718	0.5915
No log	64.0	256	2.0060	0.3718	0.5915
No log	65.0	260	1.9977	0.3718	0.5915
No log	66.0	264	1.9901	0.3718	0.5915
No log	67.0	268	1.9827	0.3718	0.5915
No log	68.0	272	1.9757	0.3718	0.5915
No log	69.0	276	1.9690	0.3718	0.5915
No log	70.0	280	1.9622	0.3718	0.5915
No log	71.0	284	1.9561	0.3718	0.5915
No log	72.0	288	1.9505	0.3718	0.5915
No log	73.0	292	1.9447	0.3718	0.5915
No log	74.0	296	1.9401	0.3718	0.5915
No log	75.0	300	1.9349	0.3863	0.5987
No log	76.0	304	1.9303	0.3863	0.5987
No log	77.0	308	1.9254	0.3863	0.5987
No log	78.0	312	1.9209	0.3863	0.5987
No log	79.0	316	1.9171	0.3863	0.5987
No log	80.0	320	1.9133	0.3863	0.5987
No log	81.0	324	1.9098	0.3863	0.5987
No log	82.0	328	1.9067	0.3718	0.5915
No log	83.0	332	1.9034	0.3718	0.5915
No log	84.0	336	1.8999	0.3718	0.5915
No log	85.0	340	1.8975	0.3718	0.5915
No log	86.0	344	1.8949	0.3718	0.5915
No log	87.0	348	1.8928	0.3718	0.5915
No log	88.0	352	1.8902	0.3718	0.5915
No log	89.0	356	1.8880	0.3718	0.5915
No log	90.0	360	1.8859	0.3718	0.5915
No log	91.0	364	1.8845	0.3718	0.5915
No log	92.0	368	1.8829	0.3718	0.5915
No log	93.0	372	1.8819	0.3718	0.5915
No log	94.0	376	1.8803	0.3718	0.5915
No log	95.0	380	1.8801	0.3718	0.5915
No log	96.0	384	1.8782	0.3718	0.5915
No log	97.0	388	1.8782	0.3718	0.5915
No log	98.0	392	1.8773	0.3718	0.5915
No log	99.0	396	1.8773	0.3718	0.5915
No log	100.0	400	1.8767	0.3718	0.5915

Framework versions

Transformers 4.34.0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.14.1

Library: transformers
Type: bart
License: mit
Created: Oct 11, 2023
Updated: Oct 11, 2023