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# camembert-ner-finetuned-jul

This model is a fine-tuned version of [Jean-Baptiste/camembert-ner](https://huggingface.co/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

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "camembert-ner-finetuned-jul", "results": []}]}
transformers pytorch tensorboard camembert token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

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



### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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 ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3