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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: tetis-textmine-2024-camembert-large-based |
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results: [] |
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widget: |
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- text: À 8 M à l’ENE du phare de Nadji, le port de pêche de Sidi Abderrahmane (36° 29,7' N — 1° 05,7' E) est construit au bord du village de Soug el Bgar (pointe Rouge). |
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example_title: Defi_TextMine |
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license: cc-by-nc-4.0 |
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# [TETIS](https://www.umr-tetis.fr) @ [Challenge TextMine 2024](https://textmine.sciencesconf.org/resource/page/id/9) |
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## This model is a NER based on Camembert-Large for the Kaggle Competition (in French): https://www.kaggle.com/competitions/defi-textmine-2024/ |
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This model could be re-use with HuggingFace transormers pipeline. To use it, please refer to its [Github](https://github.com/tetis-nlp/tetis-challenge_textmine_2024) |
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<img align="left" src="https://www.umr-tetis.fr/images/logo-header-tetis.png"> |
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| Participants | |
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|----------------------| |
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| Rémy Decoupes | |
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| Roberto Interdonato | |
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| Rodrique Kafando | |
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| Mehtab Syed Alam | |
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| Maguelonne Teisseire | |
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| Mathieu Roche | |
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| Sarah Valentin | |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# tetis-textmine-2024-camembert-large-based |
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This model is a fine-tuned version of [camembert/camembert-large](https://huggingface.co/camembert/camembert-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1106 |
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- Precision: 0.9327 |
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- Recall: 0.9471 |
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- F1: 0.9398 |
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- Accuracy: 0.9843 |
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- Aucun F1: 0.9434 |
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- Geogfeat F1: 0.9193 |
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- Geogfeat geogname F1: 0.9554 |
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- Geogname F1: 0.9133 |
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- Name geogname F1: 0.9519 |
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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: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Aucun F1 | Geogfeat F1 | Geogfeat geogname F1 | Geogname F1 | Name geogname F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:--------:|:-----------:|:--------------------:|:-----------:|:----------------:| |
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| No log | 1.0 | 192 | 0.1045 | 0.9171 | 0.9369 | 0.9269 | 0.9828 | 0.9303 | 0.8943 | 0.9509 | 0.9174 | 0.9373 | |
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| No log | 2.0 | 384 | 0.1029 | 0.9223 | 0.9471 | 0.9345 | 0.9830 | 0.9339 | 0.9170 | 0.9522 | 0.9419 | 0.9377 | |
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| 0.0072 | 3.0 | 576 | 0.0952 | 0.9136 | 0.9466 | 0.9298 | 0.9840 | 0.9226 | 0.8993 | 0.9587 | 0.9440 | 0.9571 | |
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| 0.0072 | 4.0 | 768 | 0.1054 | 0.9347 | 0.9409 | 0.9378 | 0.9838 | 0.9380 | 0.9256 | 0.9603 | 0.9164 | 0.9433 | |
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| 0.0072 | 5.0 | 960 | 0.1165 | 0.9229 | 0.9347 | 0.9288 | 0.9814 | 0.9328 | 0.9013 | 0.9441 | 0.9060 | 0.9451 | |
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| 0.0071 | 6.0 | 1152 | 0.1070 | 0.9306 | 0.9462 | 0.9383 | 0.9840 | 0.9419 | 0.9144 | 0.9487 | 0.9213 | 0.9533 | |
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| 0.0071 | 7.0 | 1344 | 0.1037 | 0.9285 | 0.9453 | 0.9368 | 0.9844 | 0.9392 | 0.9100 | 0.9534 | 0.9271 | 0.9507 | |
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| 0.0013 | 8.0 | 1536 | 0.1127 | 0.9335 | 0.9475 | 0.9405 | 0.9841 | 0.9451 | 0.9175 | 0.9505 | 0.9222 | 0.9520 | |
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| 0.0013 | 9.0 | 1728 | 0.1110 | 0.9356 | 0.9488 | 0.9422 | 0.9849 | 0.9452 | 0.9195 | 0.9571 | 0.9186 | 0.9572 | |
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| 0.0013 | 10.0 | 1920 | 0.1106 | 0.9327 | 0.9471 | 0.9398 | 0.9843 | 0.9434 | 0.9193 | 0.9554 | 0.9133 | 0.9519 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.0 |
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- Tokenizers 0.13.3 |
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