metadata

library_name: transformers
license: apache-2.0
base_model: intfloat/multilingual-e5-base
tags:
  - generated_from_trainer
  - sentence-transformers
  - text-classification
  - feature-extraction
  - generated_from_trainer
  - legal
  - taxation
  - fiscalité
  - tax
metrics:
  - accuracy
model-index:
  - name: lemone-router
    results: []
language:
  - fr
pipeline_tag: text-classification
datasets:
  - louisbrulenaudet/code-impots
  - louisbrulenaudet/code-impots-annexe-iv
  - louisbrulenaudet/code-impots-annexe-iii
  - louisbrulenaudet/code-impots-annexe-i
  - louisbrulenaudet/code-impots-annexe-ii
  - louisbrulenaudet/livre-procedures-fiscales
  - louisbrulenaudet/bofip

Lemone-Router: A Series of Fine-Tuned Classification Models for French Taxation

This model is a fine-tuned version of intfloat/multilingual-e5-base. It achieves the following results on the evaluation set:

Loss: 0.4096
Accuracy: 0.9265

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: 4.099463734610582e-05
train_batch_size: 16
eval_batch_size: 64
seed: 23
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5371	1.0	2809	0.4147	0.8680
0.3154	2.0	5618	0.3470	0.8914
0.2241	3.0	8427	0.3345	0.9147
0.1273	4.0	11236	0.3788	0.9187
0.0525	5.0	14045	0.4096	0.9265

Training Hardware

On Cloud: No
GPU Model: 1 x NVIDIA H100 NVL
CPU Model: AMD EPYC 9V84 96-Core Processor
RAM Size: 314.68 GB

Framework versions

Transformers 4.45.2
Pytorch 2.4.1+cu121
Datasets 2.21.0
Tokenizers 0.20.1

Citation

If you use this code in your research, please use the following BibTeX entry.

@misc{louisbrulenaudet2024,
  author =       {Louis Brulé Naudet},
  title =        {Lemone-Embed: A Series of Fine-Tuned Embedding Models for French Taxation},
  year =         {2024}
  howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/lemone-embed-pro}},
}

Feedback

If you have any feedback, please reach out at [email protected].