# Fill-Mask PyTorch Model (Camembert)

This model is a `fill-mask` model that was trained using the PyTorch framework and the Hugging Face Transformers library. It was utilized in Hugging Face's NLP course as an introductory model.

## Model Description

This model uses the `camembert` architecture, a variant of the RoBERTa model adapted for French. It's designed for the fill-mask task, where a portion of input text is masked and the model predicts the missing token.

## Features

- **PyTorch**: The model was implemented and trained using the PyTorch deep learning framework, which allows for dynamic computation graphs and is known for its flexibility and efficiency.
- **Safetensors**: The model utilizes Safetensors, a Python library that provides safer operations for PyTorch Tensors.
- **Transformers**: The model was built using the Hugging Face Transformers library, a state-of-the-art NLP library that provides thousands of pre-trained models and easy-to-use implementations of transformer architectures.
- **AutoTrain Compatible**: This model is compatible with Hugging Face's AutoTrain, a tool that automates the training of transformer models.

## Usage

```python
from transformers import CamembertForMaskedLM, CamembertTokenizer

tokenizer = CamembertTokenizer.from_pretrained('model-name')
model = CamembertForMaskedLM.from_pretrained('model-name')

inputs = tokenizer("Le camembert est <mask>.", return_tensors='pt')
outputs = model(**inputs)
predictions = outputs.logits
predicted_index = torch.argmax(predictions[0, mask_position]).item()
predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]