nb-bert-large-user-needs-v2

thusken/nb-bert-large-user-needs-v2

nb-bert-large-user-needs-v2 is a machine learning model from thusken. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-4.0.

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

Downloads · 30 days

5% of all-time downloads

All-time downloads

246

Public

Parameters

355M

92.3 GB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors1.4 GB · 100%

At a glance

License: cc-by-4.0
Model type: bert
Access: Public
Created: Nov 21, 2024
Updated: Nov 28, 2024
SHA: c523844c

Base models

NbAiLab/nb-bert-large

Model card

From the Hugging Face model README

nb-bert-large-user-needs-v2

This model is a fine-tuned version of NbAiLab/nb-bert-large on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.0173
Accuracy: 0.8
F1: 0.7945
Precision: 0.7947
Recall: 0.8

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: 8
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	188	0.7673	0.696	0.6619	0.6566	0.696
No log	2.0	376	0.5713	0.7707	0.7423	0.7163	0.7707
0.6847	3.0	564	0.5849	0.7653	0.7547	0.7654	0.7653
0.6847	4.0	752	0.7731	0.7467	0.7254	0.7474	0.7467
0.6847	5.0	940	0.6056	0.7733	0.7740	0.7756	0.7733
0.4443	6.0	1128	0.7752	0.792	0.7877	0.7901	0.792
0.4443	7.0	1316	1.0173	0.8	0.7945	0.7947	0.8
0.2807	8.0	1504	1.1683	0.7813	0.7789	0.7783	0.7813
0.2807	9.0	1692	1.1886	0.7893	0.7825	0.7841	0.7893
0.2807	10.0	1880	1.3052	0.776	0.7695	0.7729	0.776
0.1282	11.0	2068	1.4641	0.784	0.7769	0.7804	0.784
0.1282	12.0	2256	1.5614	0.7813	0.7716	0.7871	0.7813
0.1282	13.0	2444	1.6424	0.784	0.7774	0.7804	0.784
0.0529	14.0	2632	1.7250	0.7813	0.7767	0.7770	0.7813
0.0529	15.0	2820	1.6061	0.8	0.7934	0.8058	0.8
0.0182	16.0	3008	1.7678	0.792	0.7854	0.7908	0.792
0.0182	17.0	3196	1.8226	0.7893	0.7834	0.7849	0.7893
0.0182	18.0	3384	1.8330	0.7973	0.7906	0.7936	0.7973
0.0061	19.0	3572	1.8423	0.7947	0.7879	0.7909	0.7947
0.0061	20.0	3760	1.8536	0.7973	0.7906	0.7936	0.7973

Framework versions

Transformers 4.36.0
Pytorch 2.1.0
Datasets 2.18.0
Tokenizers 0.15.2

Type: bert
License: cc-by-4.0
Created: Nov 21, 2024
Updated: Nov 28, 2024