metadata

library_name: transformers
license: mit
base_model: almanach/camembert-base
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: camenbert-entity-recognition
    results: []

camenbert-entity-recognition

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

Loss: 0.0117
Precision: 0.0
Recall: 0.0
F1: 0.0
Accuracy: 0.9979

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	160	0.0107	0.9980
No log	2.0	320	0.0108	0.9981
No log	3.0	480	0.0112	0.9980
0.0003	4.0	640	0.0115	0.9979
0.0003	5.0	800	0.0112	0.9980
0.0003	6.0	960	0.0112	0.9981
0.0004	7.0	1120	0.0117	0.9979
0.0004	8.0	1280	0.0112	0.9980
0.0004	9.0	1440	0.0115	0.9980
0.0005	10.0	1600	0.0117	0.9979

Framework versions

Transformers 4.46.2
Pytorch 2.5.1
Datasets 3.1.0
Tokenizers 0.20.3