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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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| 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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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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<!-- 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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