metadata

license: apache-2.0
library_name: peft
tags:
  - generated_from_trainer
base_model: distilbert-base-multilingual-cased
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: multilabel_lora_distilbert_classifier_tuned_ru
    results: []

multilabel_lora_distilbert_classifier_tuned_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
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: 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