---
license: apache-2.0
language:
- fr
metrics:
- accuracy
tags:
- French
- proverb
- nlp
- bert
- fine-tune
---


# bert-base-french-europeana-cased

This model is a fine-tuned version of [bert-base-french-europeana-cased](https://huggingface.co/dbmdz/bert-base-french-europeana-cased) on a manually created dataset.
It achieves the following results on the evaluation set:
- Loss: 1.21
- Accuracy: 0.85

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy 
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2           | 1.0   | 47    | 4.15156         | 0.174    | 
...
| 1.216         | 10    | 490   | 1.2586          | 0.856    |


## How to use

```python
from transformers import pipeline, AutoTokenizer

model_checkpoint = "dbmdz/bert-base-french-europeana-cased"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)
model= "rasta/proverbes-french-IFT-7022"

generator = pipeline(task="fill-mask", model=model, tokenizer=tokenizer)

sentence = 'quand la poire est mûre, elle [MASK]'
results = generator(sentence)

```


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1