BERT_Text_classification_clean

Noodle-bg/BERT_Text_classification_clean

BERT_Text_classification_clean is a text classification model from Noodle-bg. Use it when you need a label for a piece of text. 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

Task: Text Classification
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Jun 27, 2024
Updated: Jul 1, 2024
SHA: a0e7d290

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Base models

Model card

From the Hugging Face model README

BERT_Text_classification_clean

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5562
Accuracy: 0.8708
F1: 0.8634
Precision: 0.8673
Recall: 0.8632

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: 5e-05
train_batch_size: 16
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: 150
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
2.9977	0.07	50	2.9491	0.0609	0.0334	0.0689	0.0589
2.6422	0.14	100	2.1816	0.4223	0.3423	0.4771	0.4054
1.8378	0.21	150	1.4580	0.6045	0.5381	0.5940	0.5858
1.2155	0.28	200	1.0783	0.7019	0.6600	0.6722	0.6851
0.8869	0.35	250	0.8923	0.7491	0.7237	0.7474	0.7309
0.821	0.42	300	0.8259	0.7667	0.7453	0.7527	0.7494
0.7908	0.49	350	0.8119	0.7694	0.7417	0.7745	0.7510
0.6891	0.56	400	0.7503	0.7827	0.7566	0.7834	0.7659
0.6517	0.64	450	0.7300	0.7840	0.7592	0.8025	0.7668
0.582	0.71	500	0.7140	0.8003	0.7874	0.7946	0.7868
0.5354	0.78	550	0.7101	0.7789	0.7730	0.7973	0.7731
0.6222	0.85	600	0.6393	0.8105	0.7917	0.8066	0.7941
0.5159	0.92	650	0.6774	0.7946	0.7771	0.8021	0.7792
0.5611	0.99	700	0.6016	0.8218	0.8030	0.8211	0.8064
0.3468	1.06	750	0.6555	0.8113	0.7963	0.8126	0.7972
0.3636	1.13	800	0.6447	0.8139	0.8015	0.8182	0.8014
0.2689	1.2	850	0.5984	0.8332	0.8230	0.8294	0.8233
0.3393	1.27	900	0.6076	0.8334	0.8253	0.8337	0.8254
0.3395	1.34	950	0.5933	0.8364	0.8253	0.8320	0.8250
0.2421	1.41	1000	0.5973	0.8371	0.8256	0.8369	0.8254
0.2708	1.48	1050	0.6241	0.8348	0.8244	0.8311	0.8252
0.2972	1.55	1100	0.6012	0.8395	0.8292	0.8400	0.8274
0.2694	1.62	1150	0.6092	0.8425	0.8348	0.8497	0.8352
0.2738	1.69	1200	0.5839	0.8501	0.8426	0.8474	0.8414
0.2731	1.77	1250	0.5573	0.8542	0.8446	0.8491	0.8444
0.2472	1.84	1300	0.5565	0.8546	0.8476	0.8547	0.8469
0.1901	1.91	1350	0.5555	0.8586	0.8521	0.8568	0.8519
0.217	1.98	1400	0.5737	0.8548	0.8446	0.8549	0.8447
0.1559	2.05	1450	0.5715	0.8578	0.8494	0.8559	0.8481
0.1457	2.12	1500	0.5425	0.8650	0.8570	0.8605	0.8566
0.1067	2.19	1550	0.5734	0.8655	0.8564	0.8644	0.8560
0.1183	2.26	1600	0.5509	0.8659	0.8587	0.8617	0.8585
0.1507	2.33	1650	0.5806	0.8609	0.8528	0.8561	0.8529
0.1446	2.4	1700	0.5629	0.8700	0.8633	0.8683	0.8626
0.1234	2.47	1750	0.5690	0.8667	0.8595	0.8625	0.8595
0.0921	2.54	1800	0.5597	0.8640	0.8567	0.8599	0.8566
0.0866	2.61	1850	0.5759	0.8664	0.8593	0.8631	0.8586
0.0874	2.68	1900	0.5790	0.8680	0.8599	0.8658	0.8599
0.133	2.75	1950	0.5633	0.8696	0.8625	0.8674	0.8624
0.1309	2.82	2000	0.5580	0.8712	0.8640	0.8678	0.8640
0.1011	2.9	2050	0.5584	0.8711	0.8637	0.8680	0.8636
0.0857	2.97	2100	0.5562	0.8708	0.8634	0.8673	0.8632

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.2

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Jun 27, 2024
Updated: Jul 1, 2024