BP-MM05

Anwaarma/BP-MM05

BP-MM05 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. The card lists the license as mit.

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

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At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: bert
Access: Public
Created: Dec 3, 2023
Updated: Dec 3, 2023
SHA: ca69e90f

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Base models

Model card

From the Hugging Face model README

BP-S02andInt03

This model is a fine-tuned version of Anwaarma/BP-test4 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4031
Accuracy: 0.82
F1: 0.8097

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: 2e-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: 13

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	0.0	50	1.1241	0.54	0.4220
No log	0.01	100	0.8697	0.51	0.4402
No log	0.01	150	0.7063	0.37	0.3740
No log	0.02	200	0.6895	0.51	0.4184
No log	0.02	250	0.6880	0.52	0.4467
No log	0.02	300	0.6874	0.52	0.4778
No log	0.03	350	0.6842	0.52	0.4778
No log	0.03	400	0.6889	0.5	0.4491
No log	0.04	450	0.6727	0.55	0.5398
0.7977	0.04	500	0.6617	0.59	0.5877
0.7977	0.04	550	0.6514	0.59	0.5877
0.7977	0.05	600	0.6597	0.59	0.5877
0.7977	0.05	650	0.6322	0.59	0.5877
0.7977	0.06	700	0.5898	0.57	0.5655
0.7977	0.06	750	0.5406	0.7	0.7015
0.7977	0.06	800	0.4813	0.8	0.7862
0.7977	0.07	850	0.4706	0.8	0.7862
0.7977	0.07	900	0.4743	0.79	0.7768
0.7977	0.08	950	0.4578	0.8	0.7862
0.5646	0.08	1000	0.4571	0.8	0.7862
0.5646	0.08	1050	0.4536	0.8	0.7862
0.5646	0.09	1100	0.4461	0.8	0.7862
0.5646	0.09	1150	0.4451	0.8	0.7862
0.5646	0.1	1200	0.4398	0.81	0.7956
0.5646	0.1	1250	0.4360	0.8	0.7862
0.5646	0.1	1300	0.4325	0.81	0.7956
0.5646	0.11	1350	0.4316	0.81	0.7956
0.5646	0.11	1400	0.4310	0.81	0.7956
0.5646	0.12	1450	0.4301	0.81	0.7956
0.4672	0.12	1500	0.4275	0.81	0.7956
0.4672	0.12	1550	0.4271	0.8	0.7862
0.4672	0.13	1600	0.4258	0.81	0.7956
0.4672	0.13	1650	0.4211	0.81	0.7956
0.4672	0.14	1700	0.4154	0.82	0.8097
0.4672	0.14	1750	0.4153	0.81	0.7956
0.4672	0.14	1800	0.4120	0.81	0.7956
0.4672	0.15	1850	0.4134	0.8	0.7862
0.4672	0.15	1900	0.4119	0.8	0.7862
0.4672	0.16	1950	0.4119	0.82	0.8097
0.4371	0.16	2000	0.4094	0.82	0.8097
0.4371	0.16	2050	0.4113	0.82	0.8097
0.4371	0.17	2100	0.4136	0.83	0.8259
0.4371	0.17	2150	0.4096	0.82	0.8097
0.4371	0.18	2200	0.4116	0.82	0.8097
0.4371	0.18	2250	0.4039	0.82	0.8097
0.4371	0.18	2300	0.4044	0.82	0.8097
0.4371	0.19	2350	0.4031	0.82	0.8097

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

Task: Text Classification
Library: transformers
Type: bert
License: mit
Created: Dec 3, 2023
Updated: Dec 3, 2023