bart-base-2024-10-12_13-22

PopularPenguin/bart-base-2024-10-12_13-22

bart-base-2024-10-12_13-22 is a machine learning model from PopularPenguin. 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 apache-2.0.

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

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At a glance

Library: transformers
License: apache-2.0
Model type: bart
Access: Public
Created: Oct 12, 2024
Updated: Oct 12, 2024
SHA: 4c88a717

Base models

facebook/bart-base

Datasets

arrow

Model card

From the Hugging Face model README

bart-base-2024-10-12_13-22

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

Loss: 0.3413
Gen Len: 19.9988
Bertscorer-p: 0.5693
Bertscorer-r: 0.1741
Bertscorer-f1: 0.3646
Sacrebleu-score: 10.2355
Sacrebleu-precisions: [90.1056377359695, 78.84314927189703, 71.03531269978564, 65.97921118095769]
Bleu-bp: 0.1347

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: 0.0003
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: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Gen Len	Bertscorer-p	Bertscorer-r	Bertscorer-f1	Sacrebleu-score	Sacrebleu-precisions	Bleu-bp
0.317	1.0	4772	0.2879	19.9998	0.5428	0.1582	0.3439	9.6993	[87.29083507884441, 72.83089806032642, 64.20568134269375, 58.79563532531103]	0.1386
0.1934	2.0	9544	0.2725	19.9995	0.5576	0.1608	0.3518	9.8295	[88.83556675143292, 76.0723710308905, 67.15881021479623, 61.749907205015056]	0.1351
0.1323	3.0	14316	0.2723	20.0	0.5678	0.1719	0.3627	10.1615	[89.72749492127984, 77.42060052689843, 68.79285540795546, 63.42083414479146]	0.1370
0.0882	4.0	19088	0.2759	20.0	0.5728	0.1722	0.3650	10.1777	[90.45151089248067, 79.10211769585014, 70.55075573625463, 65.16963077018467]	0.1344
0.061	5.0	23860	0.2968	20.0	0.5672	0.1735	0.3633	10.1992	[89.8170208710569, 77.72758114247924, 69.35369251771922, 64.13642380028935]	0.1366
0.0359	6.0	28632	0.3064	20.0	0.5692	0.1807	0.3681	10.3391	[90.43231298215383, 79.56742387626873, 71.96627153855555, 66.84727640514376]	0.1348
0.0229	7.0	33404	0.3159	19.9996	0.5683	0.1740	0.3641	10.3045	[89.974323617517, 78.0061867507562, 69.70321593791971, 64.46675057044337]	0.1375
0.0129	8.0	38176	0.3253	19.9999	0.5670	0.1722	0.3625	10.1527	[89.83988773004178, 78.2656326826365, 70.11705905563593, 64.89062161576781]	0.1350
0.0068	9.0	42948	0.3389	19.9994	0.5680	0.1729	0.3633	10.2220	[89.96170046739762, 78.33494108730105, 70.31016985715492, 65.2346243333951]	0.1356
0.0035	10.0	47720	0.3413	19.9988	0.5693	0.1741	0.3646	10.2355	[90.1056377359695, 78.84314927189703, 71.03531269978564, 65.97921118095769]	0.1347

Framework versions

Transformers 4.45.1
Pytorch 2.4.0
Datasets 3.0.1
Tokenizers 0.20.0

Library: transformers
Type: bart
License: apache-2.0
Created: Oct 12, 2024
Updated: Oct 12, 2024