Misinformation-Covid-LowLearningRatebert-base-german-cased

Ghunghru/Misinformation-Covid-LowLearningRatebert-base-german-cased

Misinformation-Covid-LowLearningRatebert-base-german-cased is a text classification model from Ghunghru. 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: Jan 22, 2024
Updated: Jan 22, 2024
SHA: 2b16ffb5

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Base models

Model card

From the Hugging Face model README

Misinformation-Covid-LowLearningRatebert-base-german-cased

This model is a fine-tuned version of bert-base-german-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5151
F1: 0.3793

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-07
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	F1
0.6534	1.0	189	0.6298	0.1000
0.6467	2.0	378	0.6222	0.1379
0.6302	3.0	567	0.6121	0.0784
0.6259	4.0	756	0.6042	0.0870
0.6255	5.0	945	0.5987	0.0870
0.6091	6.0	1134	0.5922	0.0909
0.6237	7.0	1323	0.5881	0.1224
0.6019	8.0	1512	0.5826	0.1277
0.6038	9.0	1701	0.5779	0.2
0.5996	10.0	1890	0.5730	0.1961
0.5858	11.0	2079	0.5678	0.2353
0.5794	12.0	2268	0.5636	0.24
0.5806	13.0	2457	0.5587	0.2264
0.5586	14.0	2646	0.5548	0.24
0.5682	15.0	2835	0.5514	0.24
0.5631	16.0	3024	0.5471	0.2353
0.5603	17.0	3213	0.5425	0.2593
0.5437	18.0	3402	0.5393	0.2593
0.5439	19.0	3591	0.5368	0.2642
0.547	20.0	3780	0.5329	0.2909
0.5408	21.0	3969	0.5297	0.3158
0.5327	22.0	4158	0.5270	0.3158
0.5194	23.0	4347	0.5256	0.3214
0.5206	24.0	4536	0.5227	0.3214
0.516	25.0	4725	0.5205	0.3214
0.5103	26.0	4914	0.5191	0.3214
0.5037	27.0	5103	0.5172	0.3214
0.4974	28.0	5292	0.5180	0.3214
0.5116	29.0	5481	0.5156	0.3214
0.5006	30.0	5670	0.5150	0.3214
0.509	31.0	5859	0.5141	0.3214
0.4832	32.0	6048	0.5150	0.3273
0.4877	33.0	6237	0.5133	0.3214
0.49	34.0	6426	0.5131	0.3158
0.4827	35.0	6615	0.5143	0.3214
0.4986	36.0	6804	0.5125	0.3214
0.4794	37.0	6993	0.5131	0.3793
0.4809	38.0	7182	0.5137	0.3793
0.4929	39.0	7371	0.5114	0.3793
0.465	40.0	7560	0.5135	0.3793
0.4867	41.0	7749	0.5121	0.3793
0.4685	42.0	7938	0.5129	0.3793
0.4643	43.0	8127	0.5142	0.3793
0.4804	44.0	8316	0.5144	0.3793
0.4779	45.0	8505	0.5141	0.3793
0.4701	46.0	8694	0.5139	0.3793
0.4619	47.0	8883	0.5146	0.3793
0.4558	48.0	9072	0.5151	0.3793
0.4824	49.0	9261	0.5152	0.3793
0.4758	50.0	9450	0.5151	0.3793

Framework versions

Transformers 4.32.1
Pytorch 2.1.2
Datasets 2.12.0
Tokenizers 0.13.3

Task: Text Classification
Library: transformers
Type: bert
License: mit
Created: Jan 22, 2024
Updated: Jan 22, 2024