training_results

logiczmaksimka/training_results

training_results is a text classification model from logiczmaksimka. 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: electra
Access: Public
Created: Nov 11, 2023
Updated: Nov 11, 2023
SHA: 0c83f5c8

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Base models

ai-forever/ruElectra-medium

Model card

From the Hugging Face model README

training_results

This model is a fine-tuned version of ai-forever/ruElectra-medium on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.6856
Accuracy: 0.7135
Recall: 0.6688
Precision: 0.7321
F1: 0.6855

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: 0.0001
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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall	Precision	F1
No log	1.0	200	1.0503	0.6462	0.5404	0.5573	0.5309
No log	2.0	400	0.9312	0.6842	0.6358	0.6068	0.5981
0.9761	3.0	600	0.9141	0.7193	0.6410	0.6629	0.6447
0.9761	4.0	800	1.1036	0.7193	0.6453	0.6843	0.6516
0.3389	5.0	1000	1.3396	0.7135	0.6512	0.7203	0.6576
0.3389	6.0	1200	1.4660	0.7251	0.6688	0.7587	0.6759
0.3389	7.0	1400	1.4835	0.7135	0.6656	0.6910	0.6640
0.1627	8.0	1600	1.8635	0.7135	0.6535	0.7441	0.6673
0.1627	9.0	1800	1.5689	0.7368	0.7140	0.7412	0.7192
0.0893	10.0	2000	1.9628	0.7047	0.6885	0.7050	0.6842
0.0893	11.0	2200	1.9155	0.7339	0.6814	0.7328	0.6995
0.0893	12.0	2400	2.0020	0.7398	0.7086	0.7351	0.7064
0.0781	13.0	2600	2.0432	0.7193	0.7005	0.7265	0.6876
0.0781	14.0	2800	1.8877	0.7544	0.7385	0.7634	0.7415
0.0435	15.0	3000	2.2208	0.7281	0.6876	0.7271	0.6871
0.0435	16.0	3200	1.9514	0.7485	0.7071	0.7438	0.7169
0.0435	17.0	3400	2.0358	0.7368	0.7551	0.7406	0.7402
0.0405	18.0	3600	2.2364	0.7310	0.6250	0.6655	0.6307
0.0405	19.0	3800	2.3225	0.7164	0.6779	0.7234	0.6868
0.0511	20.0	4000	2.1369	0.7310	0.6826	0.7670	0.7089
0.0511	21.0	4200	2.2229	0.7427	0.6981	0.7783	0.7145
0.0511	22.0	4400	2.2711	0.7222	0.6650	0.7214	0.6671
0.0382	23.0	4600	2.4241	0.7222	0.6556	0.7826	0.6834
0.0382	24.0	4800	2.0575	0.7368	0.6767	0.7238	0.6804
0.0413	25.0	5000	2.5485	0.7076	0.6681	0.6842	0.6682
0.0413	26.0	5200	2.2235	0.7222	0.6474	0.6889	0.6536
0.0413	27.0	5400	2.5252	0.7105	0.6835	0.7028	0.6793
0.035	28.0	5600	2.5843	0.7164	0.6438	0.7341	0.6654
0.035	29.0	5800	2.6856	0.7135	0.6688	0.7321	0.6855

Framework versions

Transformers 4.34.0
Pytorch 2.1.0+cu121
Datasets 2.14.5
Tokenizers 0.14.1

Task: Text Classification
Library: transformers
Type: electra
License: mit
Created: Nov 11, 2023
Updated: Nov 11, 2023