metadata
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 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
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