metadata

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 @ Challenge TextMine 2024

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

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

tetis-textmine-2024-camembert-large-based

This model is a fine-tuned version of 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