README.md

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_runews_classifier_tuned
    results: []
datasets:
  - pyteach237/news_classify
language:
  - ru
  - fr
  - en

# Model Card: DistilBERT with LoRA for Text Classification

Model Details

Model Name: DistilBERT with LoRA for Text Classification
Model Type: Transformer-based Language Model
Base Model: distilbert-base-multilingual-cased
Fine-tuning Framework: LoRA (Low-Rank Adaptation of Large Language Models)
Trained By: ABODO ELOUNDOU Brice Donald
License: Apache 2.0

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: 1.0019
• Accuracy: 0.8276
• F1: 0.8284
• Precision: 0.8317
• Recall: 0.8276

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: 0.0009143508688456378
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	91	0.5987	0.7634	0.7621	0.7648	0.7634
No log	2.0	182	0.3768	0.8693	0.8698	0.8767	0.8693
No log	3.0	273	0.2620	0.9065	0.9063	0.9093	0.9065
No log	4.0	364	0.2427	0.9202	0.9203	0.9220	0.9202
No log	5.0	455	0.2244	0.9367	0.9369	0.9387	0.9367
0.3641	6.0	546	0.2385	0.9491	0.9491	0.9495	0.9491
0.3641	7.0	637	0.2560	0.9464	0.9464	0.9465	0.9464

Framework versions

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