Modelpublic

pyteach237/multilabel_lora_distilbert_classifier_tuned_ru

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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Model Card

multilabelloradistilbertclassifiertuned_ru

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

—Loss: 1.3658
—Accuracy: 0.7845
—F1: 0.7857
—Precision: 0.7997
—Recall: 0.7845

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: 4.993596574084884e-05
—trainbatchsize: 4
—evalbatchsize: 4
—seed: 42
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
1.0622	1.0	727	0.9090	0.6025	0.5923	0.6149	0.6025
0.9449	2.0	1454	0.7451	0.6891	0.6855	0.6950	0.6891
0.7018	3.0	2181	0.6176	0.7359	0.7354	0.7377	0.7359
0.6192	4.0	2908	0.5854	0.7758	0.7751	0.7805	0.7758
0.4921	5.0	3635	0.5727	0.8061	0.8050	0.8202	0.8061
0.4091	6.0	4362	0.5019	0.8294	0.8293	0.8301	0.8294
0.3273	7.0	5089	0.4864	0.8404	0.8403	0.8409	0.8404
0.3473	8.0	5816	0.4828	0.8514	0.8512	0.8557	0.8514
0.2821	9.0	6543	0.4679	0.8597	0.8597	0.8597	0.8597
0.2599	10.0	7270	0.4874	0.8803	0.8799	0.8823	0.8803
0.2717	11.0	7997	0.4551	0.8831	0.8829	0.8832	0.8831
0.2211	12.0	8724	0.4602	0.8858	0.8856	0.8859	0.8858
0.2207	13.0	9451	0.5086	0.8845	0.8837	0.8862	0.8845
0.2166	14.0	10178	0.4795	0.8941	0.8936	0.8952	0.8941
0.1782	15.0	10905	0.4650	0.8955	0.8951	0.8959	0.8955

Framework versions

—PEFT 0.11.1
—Transformers 4.41.2
—Pytorch 2.1.2
—Datasets 2.19.2
—Tokenizers 0.19.1