metadata
license: mit
tags:
- generated_from_trainer
model-index:
- name: peft-adapter-jul
results: []
peft-adapter-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: 0.0858
- Loc: {'precision': 0.6808510638297872, 'recall': 0.7407407407407407, 'f1': 0.7095343680709535, 'number': 216}
- Misc: {'precision': 0.5416666666666666, 'recall': 0.325, 'f1': 0.40624999999999994, 'number': 40}
- Org: {'precision': 0.75, 'recall': 0.81, 'f1': 0.7788461538461539, 'number': 200}
- Per: {'precision': 0.7989130434782609, 'recall': 0.75, 'f1': 0.7736842105263159, 'number': 196}
- Overall Precision: 0.7314
- Overall Recall: 0.7393
- Overall F1: 0.7353
- Overall Accuracy: 0.9799
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: 0.0001
- 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