LiLT-RE-PT-SIN

kavg/LiLT-RE-PT-SIN

LiLT-RE-PT-SIN is a machine learning model from kavg. 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: lilt
Access: Public
Created: Mar 29, 2024
Updated: Mar 29, 2024
SHA: 744f4184

Base models

kavg/LiLT-RE-PT

Datasets

xfun

Model card

From the Hugging Face model README

checkpoints

This model is a fine-tuned version of kavg/LiLT-RE-PT on the xfun dataset. It achieves the following results on the evaluation set:

Precision: 0.3631
Recall: 0.4823
F1: 0.4143
Loss: 0.1671

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: 1e-05
train_batch_size: 8
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
training_steps: 10000

Training results

Training Loss	Epoch	Step	Precision	Recall	F1	Validation Loss
0.0907	41.67	500	0.3315	0.3106	0.3207	0.2039
0.0766	83.33	1000	0.3631	0.4823	0.4143	0.1671
0.0639	125.0	1500	0.3640	0.6086	0.4556	0.2525
0.0309	166.67	2000	0.3973	0.6010	0.4784	0.2339
0.0318	208.33	2500	0.4045	0.6414	0.4961	0.3325
0.0144	250.0	3000	0.4268	0.6187	0.5052	0.3513
0.0163	291.67	3500	0.4273	0.6086	0.5021	0.2880
0.0062	333.33	4000	0.4368	0.6288	0.5155	0.3064
0.0115	375.0	4500	0.4386	0.6313	0.5176	0.3283
0.0168	416.67	5000	0.4373	0.6162	0.5115	0.3258
0.0062	458.33	5500	0.4530	0.6086	0.5194	0.3467
0.0074	500.0	6000	0.4569	0.6162	0.5247	0.3401
0.0037	541.67	6500	0.4559	0.6136	0.5231	0.3526
0.008	583.33	7000	0.4650	0.6035	0.5253	0.3076
0.0045	625.0	7500	0.4610	0.6111	0.5255	0.3799
0.0045	666.67	8000	0.4551	0.6136	0.5226	0.3692
0.0052	708.33	8500	0.4535	0.6162	0.5225	0.3492
0.0002	750.0	9000	0.4537	0.6061	0.5189	0.4075
0.0027	791.67	9500	0.4581	0.6212	0.5273	0.3816
0.0009	833.33	10000	0.4569	0.6162	0.5247	0.3834

Framework versions

Transformers 4.38.2
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.1

Library: transformers
Type: lilt
License: mit
Created: Mar 29, 2024
Updated: Mar 29, 2024