---
license: mit
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: camembert-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
      config: fr
      split: validation
      args: fr
    metrics:
    - name: Precision
      type: precision
      value: 0.8826469710534169
    - name: Recall
      type: recall
      value: 0.8992854971115841
    - name: F1
      type: f1
      value: 0.8908885542168675
    - name: Accuracy
      type: accuracy
      value: 0.9472222222222222
---

<!-- 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. -->

# camembert-finetuned-ner

This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2199
- Precision: 0.8826
- Recall: 0.8993
- F1: 0.8909
- Accuracy: 0.9472

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2955        | 1.0   | 2500 | 0.2667          | 0.8603    | 0.8784 | 0.8693 | 0.9369   |
| 0.2089        | 2.0   | 5000 | 0.2269          | 0.8680    | 0.8953 | 0.8814 | 0.9443   |
| 0.1617        | 3.0   | 7500 | 0.2199          | 0.8826    | 0.8993 | 0.8909 | 0.9472   |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3