IKI-Category-multilabel_bge

ppsingh/IKI-Category-multilabel_bge

IKI-Category-multilabel_bge is a text classification model from ppsingh. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.

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

Downloads · 30 days

1% of all-time downloads

All-time downloads

404

Public

Parameters

109M

438 MB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors438 MB · 100%

At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: bert
Access: Public
Created: Feb 13, 2024
Updated: Feb 19, 2024
SHA: 386b1b6a

Try a prompt

Base models

BAAI/bge-base-en-v1.5

Model card

From the Hugging Face model README

IKI-Category-multilabel_bge

This model is a fine-tuned version of BAAI/bge-base-en-v1.5 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4541
Precision-micro: 0.75
Precision-samples: 0.7708
Precision-weighted: 0.7517
Recall-micro: 0.7880
Recall-samples: 0.7858
Recall-weighted: 0.7880
F1-micro: 0.7685
F1-samples: 0.7537
F1-weighted: 0.7615

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: 4.5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 200
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision-micro	Precision-samples	Precision-weighted	Recall-micro	Recall-samples	Recall-weighted	F1-micro	F1-samples	F1-weighted
0.8999	0.99	94	0.8742	0.3889	0.0272	0.1308	0.0169	0.0188	0.0169	0.0323	0.0202	0.0280
0.7377	2.0	189	0.6770	0.4727	0.4996	0.5333	0.5639	0.5782	0.5639	0.5143	0.4883	0.4998
0.5582	2.99	283	0.5552	0.5111	0.5585	0.5685	0.7229	0.7357	0.7229	0.5988	0.5959	0.6175
0.3943	4.0	378	0.4713	0.5616	0.6397	0.5869	0.7904	0.8071	0.7904	0.6567	0.6761	0.6611
0.2883	4.99	472	0.4555	0.6384	0.6969	0.6444	0.7446	0.7641	0.7446	0.6874	0.6901	0.6854
0.2112	6.0	567	0.4459	0.6443	0.6968	0.6637	0.7855	0.7942	0.7855	0.7079	0.7123	0.7068
0.1608	6.99	661	0.4212	0.6508	0.7071	0.6586	0.7904	0.7931	0.7904	0.7138	0.7161	0.7116
0.1247	8.0	756	0.4177	0.6633	0.7145	0.6650	0.7976	0.8006	0.7976	0.7243	0.7193	0.7195
0.1031	8.99	850	0.4435	0.7277	0.7523	0.7306	0.7855	0.7875	0.7855	0.7555	0.7425	0.7487
0.0851	10.0	945	0.4522	0.7380	0.7623	0.7465	0.7807	0.7795	0.7807	0.7588	0.7432	0.7516
0.074	10.99	1039	0.4548	0.7359	0.7663	0.7368	0.7855	0.7910	0.7855	0.7599	0.7490	0.7521
0.0648	12.0	1134	0.4430	0.7425	0.7676	0.7437	0.7783	0.7781	0.7783	0.76	0.7461	0.7540
0.0605	12.99	1228	0.4478	0.7366	0.7651	0.7379	0.7952	0.7948	0.7952	0.7648	0.7545	0.7579
0.0566	14.0	1323	0.4574	0.7506	0.7708	0.7519	0.7904	0.7893	0.7904	0.7700	0.7546	0.7625
0.0546	14.92	1410	0.4541	0.75	0.7708	0.7517	0.7880	0.7858	0.7880	0.7685	0.7537	0.7615

Category	Precision	Recall	F1	Suport
Active mobility	0.70	0.894	0.7908	19.0
Alternative fuels	0.804	0.865	0.833	52.0
Aviation improvements	0.700	1.00	0.824	7.0
Comprehensive transport planning	0.750	0.571	0.649	21.0
Digital solutions	0.708	0.772	0.739	22.0
Economic instruments	0.742	0.821	0.780	28.0
Education and behavioral change	0.727	0.727	0.727	11.0
Electric mobility	0.766	0.922	0.837	64.0
Freight efficiency improvements	0.768	0.650	0.703	20.0
Improve infrastructure	0.638	0.857	0.732	35.0
Land use	1.00	0.625	0.769	8.0
Other Transport Category	0.600	0.27	0.375	11.0
Public transport improvement	0.777	0.833	0.804	42.0
Shipping improvements	0.846	0.846	0.846	13.0
Transport demand management	0.666	0.40	0.500	15.0
Vehicle improvements	0.783	0.766	0.774	47.0

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Carbon Emitted: 0.0473 kg of CO2
Hours Used: 0.996 hours

Training Hardware

On Cloud: yes
GPU Model: 1 x Tesla T4
CPU Model: Intel(R) Xeon(R) CPU @ 2.30GHz
RAM Size: 12.67 GB

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.17.0
Tokenizers 0.15.1

Task: Text Classification
Library: transformers
Type: bert
License: mit
Created: Feb 13, 2024
Updated: Feb 19, 2024