| # 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] | |