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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: intfloat/multilingual-e5-base |
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tags: |
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- generated_from_trainer |
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- sentence-transformers |
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- text-classification |
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- feature-extraction |
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- generated_from_trainer |
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- legal |
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- taxation |
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- fiscalité |
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- tax |
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metrics: |
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- accuracy |
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model-index: |
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- name: lemone-router |
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results: [] |
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language: |
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- fr |
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pipeline_tag: text-classification |
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datasets: |
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- louisbrulenaudet/code-impots |
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- louisbrulenaudet/code-impots-annexe-iv |
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- louisbrulenaudet/code-impots-annexe-iii |
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- louisbrulenaudet/code-impots-annexe-i |
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- louisbrulenaudet/code-impots-annexe-ii |
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- louisbrulenaudet/livre-procedures-fiscales |
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- louisbrulenaudet/bofip |
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--- |
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<img src="assets/thumbnail.webp"> |
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# Lemone-Router: A Series of Fine-Tuned Classification Models for French Taxation |
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This model is a fine-tuned version of [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4096 |
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- Accuracy: 0.9265 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 4.099463734610582e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 64 |
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- seed: 23 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.5371 | 1.0 | 2809 | 0.4147 | 0.8680 | |
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| 0.3154 | 2.0 | 5618 | 0.3470 | 0.8914 | |
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| 0.2241 | 3.0 | 8427 | 0.3345 | 0.9147 | |
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| 0.1273 | 4.0 | 11236 | 0.3788 | 0.9187 | |
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| 0.0525 | 5.0 | 14045 | 0.4096 | 0.9265 | |
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### Training Hardware |
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- **On Cloud**: No |
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- **GPU Model**: 1 x NVIDIA H100 NVL |
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- **CPU Model**: AMD EPYC 9V84 96-Core Processor |
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- **RAM Size**: 314.68 GB |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.20.1 |
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## Citation |
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If you use this code in your research, please use the following BibTeX entry. |
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```BibTeX |
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@misc{louisbrulenaudet2024, |
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author = {Louis Brulé Naudet}, |
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title = {Lemone-Embed: A Series of Fine-Tuned Embedding Models for French Taxation}, |
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year = {2024} |
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howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/lemone-embed-pro}}, |
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} |
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``` |
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## Feedback |
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If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). |
