---
license: mit
tags:
- generated_from_trainer
model-index:
- name: peft-adapter-jul
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# peft-adapter-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: 0.0858
- 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