Update README.md

README.md

@@ -1,21 +1,21 @@
-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.
+# Fill-Mask PyTorch Model (Camembert)
 
-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.
+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.
 
-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.
+## Model Description
 
-Safetensors: The model utilizes Safetensors, a Python library that provides safer operations for PyTorch Tensors.
+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.
 
-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.
+## Features
 
-AutoTrain Compatible: This model is compatible with Hugging Face's AutoTrain, a tool that automates the training of transformer models.
+- **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
-Copy code
+## Usage
+
+```python
 from transformers import CamembertForMaskedLM, CamembertTokenizer
 
 tokenizer = CamembertTokenizer.from_pretrained('model-name')
@@ -26,10 +26,3 @@ 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]
-Replace 'model-name' with the name of this model.
-
-Limitations
-As with any machine learning model, this model has its limitations. Since it is trained on a specific dataset, it may not perform well on texts with significantly different styles or topics. In addition, while the Transformers library and the model are optimized for a wide range of NLP tasks, some fine-tuning or adaptation may be required for more specific or niche applications.
-
-Conclusion
-This model serves as a solid introduction to fill-mask tasks, PyTorch, Transformers, and Hugging Face's tools and resources. With its ease of use and high performance, it can be a great resource for both learning and application.