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# peft-prefix-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: 0.0857
- Loc: {'precision': 0.711864406779661, 'recall': 0.7777777777777778, 'f1': 0.7433628318584071, 'number': 216}
- Misc: {'precision': 0.6956521739130435, 'recall': 0.4, 'f1': 0.507936507936508, 'number': 40}
- Org: {'precision': 0.8097560975609757, 'recall': 0.83, 'f1': 0.8197530864197532, 'number': 200}
- Per: {'precision': 0.8260869565217391, 'recall': 0.7755102040816326, 'f1': 0.8, 'number': 196}
- Overall Precision: 0.7747
- Overall Recall: 0.7699
- Overall F1: 0.7723
- Overall Accuracy: 0.9822

## 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: 0.0002
- 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 4.26.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "peft-prefix-jul", "results": []}]}
tensorboard generated_from_trainer license:mit region:us
TAGS
#tensorboard #generated_from_trainer #license-mit #region-us 




# peft-prefix-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: 0.0857
- Loc: {'precision': 0.711864406779661, 'recall': 0.7777777777777778, 'f1': 0.7433628318584071, 'number': 216}
- Misc: {'precision': 0.6956521739130435, 'recall': 0.4, 'f1': 0.507936507936508, 'number': 40}
- Org: {'precision': 0.8097560975609757, 'recall': 0.83, 'f1': 0.8197530864197532, 'number': 200}
- Per: {'precision': 0.8260869565217391, 'recall': 0.7755102040816326, 'f1': 0.8, 'number': 196}
- Overall Precision: 0.7747
- Overall Recall: 0.7699
- Overall F1: 0.7723
- Overall Accuracy: 0.9822

## 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: 0.0002
- 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 4.26.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

# peft-prefix-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: 0.0857
- Loc: {'precision': 0.711864406779661, 'recall': 0.7777777777777778, 'f1': 0.7433628318584071, 'number': 216}
- Misc: {'precision': 0.6956521739130435, 'recall': 0.4, 'f1': 0.507936507936508, 'number': 40}
- Org: {'precision': 0.8097560975609757, 'recall': 0.83, 'f1': 0.8197530864197532, 'number': 200}
- Per: {'precision': 0.8260869565217391, 'recall': 0.7755102040816326, 'f1': 0.8, 'number': 196}
- Overall Precision: 0.7747
- Overall Recall: 0.7699
- Overall F1: 0.7723
- Overall Accuracy: 0.9822 ## 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: 0.0002
- 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 4.26.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
TAGS
#tensorboard #generated_from_trainer #license-mit #region-us 
 # peft-prefix-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: 0.0857
- Loc: {'precision': 0.711864406779661, 'recall': 0.7777777777777778, 'f1': 0.7433628318584071, 'number': 216}
- Misc: {'precision': 0.6956521739130435, 'recall': 0.4, 'f1': 0.507936507936508, 'number': 40}
- Org: {'precision': 0.8097560975609757, 'recall': 0.83, 'f1': 0.8197530864197532, 'number': 200}
- Per: {'precision': 0.8260869565217391, 'recall': 0.7755102040816326, 'f1': 0.8, 'number': 196}
- Overall Precision: 0.7747
- Overall Recall: 0.7699
- Overall F1: 0.7723
- Overall Accuracy: 0.9822 ## 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: 0.0002
- 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 4.26.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3