docs: update README.md (#5)

Huggingface -> Hugging Face

README.md

@@ -14,7 +14,7 @@
 </div>
 
 > [!IMPORTANT] 
-> **We are thrilled to release [`Llama-3-8B-Web`](https://huggingface.co/McGill-NLP/Llama-3-8B-Web), the most capable agent built with 🦙 Llama 3 and finetuned for web navigation with dialogue. You can download the agent from the 🤗 [Huggingface Model Hub](https://huggingface.co/McGill-NLP/Llama-3-8B-Web).**
+> **We are thrilled to release [`Llama-3-8B-Web`](https://huggingface.co/McGill-NLP/Llama-3-8B-Web), the most capable agent built with 🦙 Llama 3 and finetuned for web navigation with dialogue. You can download the agent from the 🤗 [Hugging Face Model Hub](https://huggingface.co/McGill-NLP/Llama-3-8B-Web).**
 
 | `WebLlama` helps you build powerful agents, powered by Meta Llama 3, for browsing the web on your behalf | Our first model, [`Llama-3-8B-Web`](https://huggingface.co/McGill-NLP/Llama-3-8B-Web), surpasses GPT-4V (`*`zero-shot) by 18% on [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) |
 |:---: | :---: |
@@ -33,15 +33,15 @@
 ## Modeling
 
 > [!NOTE]
-> The model is available on the 🤗 Hugging Face Model Hub as [`McGill-NLP/Llama-3-8B-Web`](https://huggingface.co/McGill-NLP/Llama-3-8B-Web). The training and evaluation data is available on [Huggingface Hub as `McGill-NLP/WebLINX`](https://huggingface.co/datasets/McGill-NLP/WebLINX).
+> The model is available on the 🤗 Hugging Face Model Hub as [`McGill-NLP/Llama-3-8B-Web`](https://huggingface.co/McGill-NLP/Llama-3-8B-Web). The training and evaluation data is available on [Hugging Face Hub as `McGill-NLP/WebLINX`](https://huggingface.co/datasets/McGill-NLP/WebLINX).
 
 Our first agent is a finetuned [`Meta-Llama-3-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) model, which was recently released by Meta GenAI team. We have finetuned this model on the [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) dataset, which contains over 100K instances of web navigation and dialogue, each collected and verified by expert annotators. We use a 24K curated subset for training the data.
 
 ![Comparison of Llama-3-Web, GPT-4V, GPT-3.5 and MindAct](assets/LlamaAndGPTAndMindAct.png)
 
 **It surpasses GPT-4V (zero-shot `*`) by over 18% on the [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) benchmark**, achieving an overall score of 28.8% on the out-of-domain test splits (compared to 10.5% for GPT-4V). It chooses more useful links (34.1% vs 18.9% *seg-F1*), clicks on more relevant elements (27.1% vs 13.6% *IoU*) and formulates more aligned responses (37.5% vs 3.1% *chr-F1*).
 
-It's extremely straightforward to use the model via Huggingface's `transformers`, `datasets` and `hub` libraries:
+It's extremely straightforward to use the model via Hugging Face's `transformers`, `datasets` and `hub` libraries:
 
 ```python
 from datasets import load_dataset