rdecoupes/tetis-textmine-2024-camembert-large-based

---

license: cc-by-nc-4.0

TETIS

---

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