camembert-ner-finetuned-jul

This model is a fine-tuned version of Jean-Baptiste/camembert-ner on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1177
Loc: {'precision': 0.7309417040358744, 'recall': 0.7546296296296297, 'f1': 0.7425968109339408, 'number': 216}
Misc: {'precision': 0.5862068965517241, 'recall': 0.425, 'f1': 0.4927536231884058, 'number': 40}
Org: {'precision': 0.8333333333333334, 'recall': 0.825, 'f1': 0.8291457286432161, 'number': 200}
Per: {'precision': 0.7823834196891192, 'recall': 0.7704081632653061, 'f1': 0.776349614395887, 'number': 196}
Overall Precision: 0.7714
Overall Recall: 0.7607
Overall F1: 0.7660
Overall Accuracy: 0.9812

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: 16
eval_batch_size: 16
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	Loc	Misc	Org	Per	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
No log	1.0	408	0.0602	{'precision': 0.6894977168949772, 'recall': 0.7330097087378641, 'f1': 0.7105882352941175, 'number': 206}	{'precision': 0.8461538461538461, 'recall': 0.2972972972972973, 'f1': 0.44000000000000006, 'number': 37}	{'precision': 0.7472527472527473, 'recall': 0.7472527472527473, 'f1': 0.7472527472527473, 'number': 182}	{'precision': 0.8195876288659794, 'recall': 0.7571428571428571, 'f1': 0.787128712871287, 'number': 210}	0.7516	0.7197	0.7353	0.9830
0.0903	2.0	816	0.0568	{'precision': 0.776255707762557, 'recall': 0.8252427184466019, 'f1': 0.7999999999999999, 'number': 206}	{'precision': 0.5217391304347826, 'recall': 0.32432432432432434, 'f1': 0.4, 'number': 37}	{'precision': 0.7731958762886598, 'recall': 0.8241758241758241, 'f1': 0.7978723404255318, 'number': 182}	{'precision': 0.822429906542056, 'recall': 0.8380952380952381, 'f1': 0.830188679245283, 'number': 210}	0.7815	0.8	0.7907	0.9845
0.0357	3.0	1224	0.0631	{'precision': 0.7339055793991416, 'recall': 0.8300970873786407, 'f1': 0.7790432801822323, 'number': 206}	{'precision': 0.6363636363636364, 'recall': 0.3783783783783784, 'f1': 0.4745762711864407, 'number': 37}	{'precision': 0.7969543147208121, 'recall': 0.8626373626373627, 'f1': 0.8284960422163589, 'number': 182}	{'precision': 0.8317757009345794, 'recall': 0.8476190476190476, 'f1': 0.839622641509434, 'number': 210}	0.7808	0.8189	0.7994	0.9851
0.0201	4.0	1632	0.0761	{'precision': 0.772093023255814, 'recall': 0.8058252427184466, 'f1': 0.7885985748218527, 'number': 206}	{'precision': 0.6, 'recall': 0.32432432432432434, 'f1': 0.4210526315789474, 'number': 37}	{'precision': 0.8263157894736842, 'recall': 0.8626373626373627, 'f1': 0.8440860215053764, 'number': 182}	{'precision': 0.8293838862559242, 'recall': 0.8333333333333334, 'f1': 0.8313539192399049, 'number': 210}	0.8019	0.8031	0.8025	0.9846
0.0113	5.0	2040	0.0745	{'precision': 0.7477876106194691, 'recall': 0.8203883495145631, 'f1': 0.7824074074074074, 'number': 206}	{'precision': 0.4666666666666667, 'recall': 0.3783783783783784, 'f1': 0.417910447761194, 'number': 37}	{'precision': 0.8229166666666666, 'recall': 0.8681318681318682, 'f1': 0.8449197860962567, 'number': 182}	{'precision': 0.8388625592417062, 'recall': 0.8428571428571429, 'f1': 0.840855106888361, 'number': 210}	0.7860	0.8157	0.8006	0.9849
0.0113	6.0	2448	0.0815	{'precision': 0.7654867256637168, 'recall': 0.8398058252427184, 'f1': 0.8009259259259259, 'number': 206}	{'precision': 0.43333333333333335, 'recall': 0.35135135135135137, 'f1': 0.3880597014925374, 'number': 37}	{'precision': 0.8253968253968254, 'recall': 0.8571428571428571, 'f1': 0.8409703504043127, 'number': 182}	{'precision': 0.8673469387755102, 'recall': 0.8095238095238095, 'f1': 0.8374384236453202, 'number': 210}	0.7988	0.8063	0.8025	0.9844
0.0085	7.0	2856	0.0850	{'precision': 0.7579908675799086, 'recall': 0.8058252427184466, 'f1': 0.7811764705882352, 'number': 206}	{'precision': 0.5416666666666666, 'recall': 0.35135135135135137, 'f1': 0.4262295081967213, 'number': 37}	{'precision': 0.828125, 'recall': 0.8736263736263736, 'f1': 0.8502673796791443, 'number': 182}	{'precision': 0.8805970149253731, 'recall': 0.8428571428571429, 'f1': 0.8613138686131387, 'number': 210}	0.8097	0.8110	0.8104	0.9853
0.0045	8.0	3264	0.0846	{'precision': 0.7321428571428571, 'recall': 0.7961165048543689, 'f1': 0.7627906976744185, 'number': 206}	{'precision': 0.4642857142857143, 'recall': 0.35135135135135137, 'f1': 0.39999999999999997, 'number': 37}	{'precision': 0.8172043010752689, 'recall': 0.8351648351648352, 'f1': 0.8260869565217392, 'number': 182}	{'precision': 0.8756218905472637, 'recall': 0.8380952380952381, 'f1': 0.856447688564477, 'number': 210}	0.7903	0.7953	0.7928	0.9847
0.0044	9.0	3672	0.0845	{'precision': 0.7614678899082569, 'recall': 0.8058252427184466, 'f1': 0.7830188679245284, 'number': 206}	{'precision': 0.48148148148148145, 'recall': 0.35135135135135137, 'f1': 0.40625, 'number': 37}	{'precision': 0.8297872340425532, 'recall': 0.8571428571428571, 'f1': 0.8432432432432433, 'number': 182}	{'precision': 0.8811881188118812, 'recall': 0.8476190476190476, 'f1': 0.8640776699029127, 'number': 210}	0.8079	0.8079	0.8079	0.9854
0.0031	10.0	4080	0.0855	{'precision': 0.7568807339449541, 'recall': 0.8009708737864077, 'f1': 0.7783018867924528, 'number': 206}	{'precision': 0.4482758620689655, 'recall': 0.35135135135135137, 'f1': 0.393939393939394, 'number': 37}	{'precision': 0.8297872340425532, 'recall': 0.8571428571428571, 'f1': 0.8432432432432433, 'number': 182}	{'precision': 0.8855721393034826, 'recall': 0.8476190476190476, 'f1': 0.8661800486618005, 'number': 210}	0.8050	0.8063	0.8057	0.9852

Framework versions

Transformers 4.29.1
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3

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camembert-ner-finetuned-jul

This model is a fine-tuned version of Jean-Baptiste/camembert-ner on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1177

Loc: {'precision': 0.7309417040358744, 'recall': 0.7546296296296297, 'f1': 0.7425968109339408, 'number': 216}

Misc: {'precision': 0.5862068965517241, 'recall': 0.425, 'f1': 0.4927536231884058, 'number': 40}

Org: {'precision': 0.8333333333333334, 'recall': 0.825, 'f1': 0.8291457286432161, 'number': 200}

Per: {'precision': 0.7823834196891192, 'recall': 0.7704081632653061, 'f1': 0.776349614395887, 'number': 196}

Overall Precision: 0.7714

Overall Recall: 0.7607

Overall F1: 0.7660

Overall Accuracy: 0.9812

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05

train_batch_size: 16

eval_batch_size: 16

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	Loc	Misc	Org	Per	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
No log	1.0	408	0.0602	{'precision': 0.6894977168949772, 'recall': 0.7330097087378641, 'f1': 0.7105882352941175, 'number': 206}	{'precision': 0.8461538461538461, 'recall': 0.2972972972972973, 'f1': 0.44000000000000006, 'number': 37}	{'precision': 0.7472527472527473, 'recall': 0.7472527472527473, 'f1': 0.7472527472527473, 'number': 182}	{'precision': 0.8195876288659794, 'recall': 0.7571428571428571, 'f1': 0.787128712871287, 'number': 210}	0.7516	0.7197	0.7353	0.9830
0.0903	2.0	816	0.0568	{'precision': 0.776255707762557, 'recall': 0.8252427184466019, 'f1': 0.7999999999999999, 'number': 206}	{'precision': 0.5217391304347826, 'recall': 0.32432432432432434, 'f1': 0.4, 'number': 37}	{'precision': 0.7731958762886598, 'recall': 0.8241758241758241, 'f1': 0.7978723404255318, 'number': 182}	{'precision': 0.822429906542056, 'recall': 0.8380952380952381, 'f1': 0.830188679245283, 'number': 210}	0.7815	0.8	0.7907	0.9845
0.0357	3.0	1224	0.0631	{'precision': 0.7339055793991416, 'recall': 0.8300970873786407, 'f1': 0.7790432801822323, 'number': 206}	{'precision': 0.6363636363636364, 'recall': 0.3783783783783784, 'f1': 0.4745762711864407, 'number': 37}	{'precision': 0.7969543147208121, 'recall': 0.8626373626373627, 'f1': 0.8284960422163589, 'number': 182}	{'precision': 0.8317757009345794, 'recall': 0.8476190476190476, 'f1': 0.839622641509434, 'number': 210}	0.7808	0.8189	0.7994	0.9851
0.0201	4.0	1632	0.0761	{'precision': 0.772093023255814, 'recall': 0.8058252427184466, 'f1': 0.7885985748218527, 'number': 206}	{'precision': 0.6, 'recall': 0.32432432432432434, 'f1': 0.4210526315789474, 'number': 37}	{'precision': 0.8263157894736842, 'recall': 0.8626373626373627, 'f1': 0.8440860215053764, 'number': 182}	{'precision': 0.8293838862559242, 'recall': 0.8333333333333334, 'f1': 0.8313539192399049, 'number': 210}	0.8019	0.8031	0.8025	0.9846
0.0113	5.0	2040	0.0745	{'precision': 0.7477876106194691, 'recall': 0.8203883495145631, 'f1': 0.7824074074074074, 'number': 206}	{'precision': 0.4666666666666667, 'recall': 0.3783783783783784, 'f1': 0.417910447761194, 'number': 37}	{'precision': 0.8229166666666666, 'recall': 0.8681318681318682, 'f1': 0.8449197860962567, 'number': 182}	{'precision': 0.8388625592417062, 'recall': 0.8428571428571429, 'f1': 0.840855106888361, 'number': 210}	0.7860	0.8157	0.8006	0.9849
0.0113	6.0	2448	0.0815	{'precision': 0.7654867256637168, 'recall': 0.8398058252427184, 'f1': 0.8009259259259259, 'number': 206}	{'precision': 0.43333333333333335, 'recall': 0.35135135135135137, 'f1': 0.3880597014925374, 'number': 37}	{'precision': 0.8253968253968254, 'recall': 0.8571428571428571, 'f1': 0.8409703504043127, 'number': 182}	{'precision': 0.8673469387755102, 'recall': 0.8095238095238095, 'f1': 0.8374384236453202, 'number': 210}	0.7988	0.8063	0.8025	0.9844
0.0085	7.0	2856	0.0850	{'precision': 0.7579908675799086, 'recall': 0.8058252427184466, 'f1': 0.7811764705882352, 'number': 206}	{'precision': 0.5416666666666666, 'recall': 0.35135135135135137, 'f1': 0.4262295081967213, 'number': 37}	{'precision': 0.828125, 'recall': 0.8736263736263736, 'f1': 0.8502673796791443, 'number': 182}	{'precision': 0.8805970149253731, 'recall': 0.8428571428571429, 'f1': 0.8613138686131387, 'number': 210}	0.8097	0.8110	0.8104	0.9853
0.0045	8.0	3264	0.0846	{'precision': 0.7321428571428571, 'recall': 0.7961165048543689, 'f1': 0.7627906976744185, 'number': 206}	{'precision': 0.4642857142857143, 'recall': 0.35135135135135137, 'f1': 0.39999999999999997, 'number': 37}	{'precision': 0.8172043010752689, 'recall': 0.8351648351648352, 'f1': 0.8260869565217392, 'number': 182}	{'precision': 0.8756218905472637, 'recall': 0.8380952380952381, 'f1': 0.856447688564477, 'number': 210}	0.7903	0.7953	0.7928	0.9847
0.0044	9.0	3672	0.0845	{'precision': 0.7614678899082569, 'recall': 0.8058252427184466, 'f1': 0.7830188679245284, 'number': 206}	{'precision': 0.48148148148148145, 'recall': 0.35135135135135137, 'f1': 0.40625, 'number': 37}	{'precision': 0.8297872340425532, 'recall': 0.8571428571428571, 'f1': 0.8432432432432433, 'number': 182}	{'precision': 0.8811881188118812, 'recall': 0.8476190476190476, 'f1': 0.8640776699029127, 'number': 210}	0.8079	0.8079	0.8079	0.9854
0.0031	10.0	4080	0.0855	{'precision': 0.7568807339449541, 'recall': 0.8009708737864077, 'f1': 0.7783018867924528, 'number': 206}	{'precision': 0.4482758620689655, 'recall': 0.35135135135135137, 'f1': 0.393939393939394, 'number': 37}	{'precision': 0.8297872340425532, 'recall': 0.8571428571428571, 'f1': 0.8432432432432433, 'number': 182}	{'precision': 0.8855721393034826, 'recall': 0.8476190476190476, 'f1': 0.8661800486618005, 'number': 210}	0.8050	0.8063	0.8057	0.9852

Framework versions

Transformers 4.29.1

Pytorch 2.0.0+cu118

Datasets 2.12.0

Tokenizers 0.13.3

fgiauna/camembert-ner-finetuned-jul

camembert-ner-finetuned-jul

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

camembert-ner-finetuned-jul

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions