vihealthbert-w_dual-ViNLI

vimednli/vihealthbert-w_dual-ViNLI

vihealthbert-w_dual-ViNLI is a machine learning model from vimednli. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

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

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At a glance

Model type: roberta
Access: Public
Created: Sep 10, 2024
Updated: Sep 10, 2024
SHA: 0742a593

Base models

demdecuong/vihealthbert-base-word

Datasets

tmnam20/ViNLI

Model card

From the Hugging Face model README

vihealthbert-w_dual-ViNLI

This model is a fine-tuned version of demdecuong/vihealthbert-base-word on the tmnam20/ViNLI dataset. It achieves the following results on the evaluation set:

Loss: 2.6042
Accuracy: 0.5919

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-05
train_batch_size: 32
eval_batch_size: 16
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: 30000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
5.8126	15.625	1000	3.5461	0.4450
2.605	31.25	2000	2.7789	0.5404
1.5924	46.875	3000	2.5432	0.5809
1.2233	62.5	4000	2.6662	0.5567
0.9236	78.125	5000	2.4691	0.5927
0.7193	93.75	6000	2.4053	0.6027
0.6259	109.375	7000	2.5938	0.5782
0.5082	125.0	8000	2.4809	0.6137
0.4438	140.625	9000	2.7056	0.5819
0.4075	156.25	10000	2.6501	0.5946
0.3571	171.875	11000	2.5337	0.6082

Framework versions

Transformers 4.40.2
Pytorch 2.0.1+cu118
Datasets 2.21.0
Tokenizers 0.19.1

Type: roberta
Created: Sep 10, 2024
Updated: Sep 10, 2024