final_V2-bert-after-adding-new-words-text-classification-model

AmirlyPhd/final_V2-bert-after-adding-new-words-text-classification-model

final_V2-bert-after-adding-new-words-text-classification-model is a text classification model from AmirlyPhd. 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: May 8, 2024
Updated: May 8, 2024
SHA: 10ab3b5e

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Model card

From the Hugging Face model README

final_V2-bert-after-adding-new-words-text-classification-model

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1494
Accuracy: 0.9716
F1: 0.8348
Precision: 0.8317
Recall: 0.8385

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: 100
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
1.8136	0.11	50	1.7501	0.3470	0.1733	0.3034	0.1944
0.9023	0.22	100	1.2121	0.5723	0.3083	0.3496	0.3189
0.5924	0.33	150	0.9662	0.6667	0.3919	0.4265	0.4037
0.4218	0.44	200	0.4848	0.8813	0.6427	0.6492	0.6413
0.2025	0.55	250	0.3807	0.9021	0.6677	0.6538	0.6829
0.1609	0.66	300	0.3360	0.9147	0.6763	0.6727	0.6822
0.2035	0.76	350	0.3705	0.8991	0.6711	0.6589	0.6838
0.1208	0.87	400	0.2140	0.9565	0.8218	0.8137	0.8323
0.1313	0.98	450	0.6818	0.8704	0.6779	0.7179	0.6859
0.1576	1.09	500	0.2508	0.9212	0.7443	0.7888	0.7311
0.0593	1.2	550	0.2091	0.9552	0.8193	0.8179	0.8227
0.0705	1.31	600	0.2010	0.9552	0.8154	0.8091	0.8225
0.0637	1.42	650	0.1985	0.9573	0.8187	0.8115	0.8275
0.0619	1.53	700	0.2306	0.9541	0.8241	0.8194	0.8301
0.0582	1.64	750	0.2001	0.9609	0.8280	0.8250	0.8320
0.1132	1.75	800	0.1439	0.9680	0.8324	0.8284	0.8367
0.0416	1.86	850	0.1558	0.9680	0.8333	0.8301	0.8369
0.0371	1.97	900	0.2242	0.9595	0.8280	0.8235	0.8345
0.0428	2.07	950	0.1907	0.9617	0.8303	0.8262	0.8356
0.0388	2.18	1000	0.1784	0.9658	0.8319	0.8266	0.8383
0.0335	2.29	1050	0.1735	0.9675	0.8323	0.8266	0.8390
0.0361	2.4	1100	0.1921	0.9636	0.8283	0.8219	0.8360
0.0126	2.51	1150	0.2200	0.9614	0.8294	0.8274	0.8327
0.003	2.62	1200	0.2251	0.9614	0.8296	0.8262	0.8346
0.0029	2.73	1250	0.1750	0.9694	0.8348	0.8314	0.8388
0.0137	2.84	1300	0.1775	0.9686	0.8345	0.8300	0.8397
0.0184	2.95	1350	0.1860	0.9675	0.8337	0.8293	0.8391

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2

Task: Text Classification
Library: transformers
Type: bert
Created: May 8, 2024
Updated: May 8, 2024