---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tetis-textmine-2024-camembert-large-based
  results: []
widget:
- 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).
  example_title: Defi_TextMine

---

---
license: cc-by-nc-4.0
---
# [TETIS](https://www.umr-tetis.fr) @ [Challenge TextMine 2024](https://textmine.sciencesconf.org/resource/page/id/9)

---
## This model is a NER based on Camembert-Large for the Kaggle Competition (in French): https://www.kaggle.com/competitions/defi-textmine-2024/

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)
---


<img align="left" src="https://www.umr-tetis.fr/images/logo-header-tetis.png">

| Participants         |
|----------------------|
| Rémy Decoupes        |
| Roberto Interdonato  |
| Rodrique Kafando     |
| Mehtab Syed Alam     |
| Maguelonne Teisseire |
| Mathieu Roche        |
| Sarah Valentin       |

---



<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# tetis-textmine-2024-camembert-large-based

This model is a fine-tuned version of [camembert/camembert-large](https://huggingface.co/camembert/camembert-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1106
- Precision: 0.9327
- Recall: 0.9471
- F1: 0.9398
- Accuracy: 0.9843
- Aucun F1: 0.9434
- Geogfeat F1: 0.9193
- Geogfeat geogname F1: 0.9554
- Geogname F1: 0.9133
- Name geogname F1: 0.9519

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy | Aucun F1 | Geogfeat F1 | Geogfeat geogname F1 | Geogname F1 | Name geogname F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:--------:|:-----------:|:--------------------:|:-----------:|:----------------:|
| 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           |
| 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           |
| 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           |
| 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           |
| 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           |
| 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           |
| 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           |
| 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           |
| 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           |
| 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           |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3