S04

Anwaarma/S04

S04 is a text classification model from Anwaarma. Use it when you need a label for a piece of text. It is set up for transformers.

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

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At a glance

Task: Text Classification
Library: transformers
Model type: bert
Access: Public
Created: Dec 24, 2023
Updated: Dec 24, 2023
SHA: 221bc5fa

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Base models

Anwaarma/Merged-Server-praj

Model card

From the Hugging Face model README

S04

This model is a fine-tuned version of Anwaarma/Merged-Server-praj on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5249
Accuracy: 0.71
F1: 0.8304

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: 4e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	0.01	50	0.6296	0.65	0.6505
No log	0.01	100	0.6280	0.67	0.6697
No log	0.02	150	0.6210	0.67	0.6665
No log	0.03	200	0.6583	0.65	0.6505
No log	0.03	250	0.6650	0.65	0.6505
No log	0.04	300	0.6613	0.67	0.6697
No log	0.04	350	0.6663	0.65	0.6502
No log	0.05	400	0.6704	0.67	0.6702
No log	0.06	450	0.6570	0.68	0.6794
0.6123	0.06	500	0.6430	0.65	0.6502
0.6123	0.07	550	0.6558	0.64	0.6404
0.6123	0.08	600	0.6662	0.64	0.64
0.6123	0.08	650	0.6547	0.64	0.6406
0.6123	0.09	700	0.6407	0.66	0.6605
0.6123	0.09	750	0.6238	0.66	0.6605
0.6123	0.1	800	0.6223	0.68	0.6794
0.6123	0.11	850	0.6006	0.66	0.6604
0.6123	0.11	900	0.6294	0.68	0.6773
0.6123	0.12	950	0.6195	0.66	0.66
0.6014	0.13	1000	0.6119	0.65	0.6505
0.6014	0.13	1050	0.6230	0.67	0.6702
0.6014	0.14	1100	0.6410	0.69	0.6905
0.6014	0.14	1150	0.6306	0.67	0.6705
0.6014	0.15	1200	0.6476	0.7	0.6994
0.6014	0.16	1250	0.6244	0.67	0.6705
0.6014	0.16	1300	0.6078	0.69	0.6897
0.6014	0.17	1350	0.5869	0.67	0.6705
0.6014	0.18	1400	0.6164	0.67	0.6665
0.6014	0.18	1450	0.6054	0.65	0.6505
0.5906	0.19	1500	0.5947	0.67	0.6705
0.5906	0.19	1550	0.5765	0.69	0.6905
0.5906	0.2	1600	0.5677	0.69	0.6905
0.5906	0.21	1650	0.5828	0.7	0.7005
0.5906	0.21	1700	0.6249	0.67	0.6689
0.5906	0.22	1750	0.5833	0.69	0.6905
0.5906	0.23	1800	0.5838	0.68	0.6804
0.5906	0.23	1850	0.5923	0.7	0.7004
0.5906	0.24	1900	0.5749	0.69	0.6905
0.5906	0.25	1950	0.5769	0.7	0.7004
0.5736	0.25	2000	0.5706	0.7	0.7005
0.5736	0.26	2050	0.5967	0.69	0.6897
0.5736	0.26	2100	0.5866	0.69	0.6897
0.5736	0.27	2150	0.5901	0.7	0.7
0.5736	0.28	2200	0.5771	0.7	0.7004
0.5736	0.28	2250	0.5616	0.69	0.6905

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.0
Tokenizers 0.15.0

Task: Text Classification
Library: transformers
Type: bert
Created: Dec 24, 2023
Updated: Dec 24, 2023