Update README.md to call "WebLINX 1.0"

README.md

@@ -22,7 +22,7 @@
 | `WebLlama` | The goal of our project is to build effective human-centric agents for browsing the web. We don't want to replace users, but equip them with powerful assistants. |
 |:---: | :---|
 | Modeling | We are build on top of cutting edge libraries for training Llama agents on web navigation tasks. We will provide training scripts, optimized configs, and instructions for training cutting-edge Llamas. |
-| Evaluation | Benchmarks for testing Llama models on real-world web browsing. This include *human-centric* browsing through dialogue ([`WebLINX`](https://mcgill-nlp.github.io/weblinx/)), and we will soon add more benchmarks for automatic web navigation (e.g. Mind2Web). |
+| Evaluation | Benchmarks for testing Llama models on real-world web browsing. This include *human-centric* browsing through dialogue ([`WebLINX 1.0`](https://mcgill-nlp.github.io/weblinx/)), and we will soon add more benchmarks for automatic web navigation (e.g. Mind2Web). |
 | Data | Our first model is finetuned on over 24K instances of web interactions, including `click`, `textinput`, `submit`, and dialogue acts. We want to continuously curate, compile and release datasets for training better agents. |
 | Deployment | We want to make it easy to integrate Llama models with existing deployment platforms, including Playwright, Selenium, and BrowserGym. We are currently focusing on making this a reality. |
 
@@ -60,11 +60,11 @@ We use Llama-3-8B-Web, a model finetuned on WebLINX demonstrations \citep{lu_202
 > [!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 [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.
+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 1.0`](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 surpasses GPT-4V (zero-shot `*`) by over 18% on the [`WebLINX 1.0`](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 Hugging Face's `transformers`, `datasets` and `hub` libraries:
 
@@ -114,7 +114,7 @@ streamlit run app/Results.py
 
 ## Data
 
-Although the 24K training examples from [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) provide a good starting point for training a capable agent, we believe that more data is needed to train agents that can generalize to a wide range of web navigation tasks. Although it has been trained and evaluated on 150 websites, there are millions of websites that has never been seen by the model, with new ones being created every day. 
+Although the 24K training examples from [`WebLINX 1.0`](https://mcgill-nlp.github.io/weblinx/) provide a good starting point for training a capable agent, we believe that more data is needed to train agents that can generalize to a wide range of web navigation tasks. Although it has been trained and evaluated on 150 websites, there are millions of websites that has never been seen by the model, with new ones being created every day. 
 
 **This motivates us to continuously curate, compile and release datasets for training better agents.** As an immediate next step, we will be incorporating `Mind2Web`'s training data into the equation, which also covers over 100 websites.
 
@@ -131,10 +131,10 @@ At the moment, we offer the following integrations:
 
 ## Code
 
-The code for finetuning the model and evaluating it on the [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) benchmark is available now. 
-* **Modeling**: You can find the detailed instructions in [modeling](modeling/README.md) for training `Llama-3-8B-Web` on the `WebLINX` dataset.
+The code for finetuning the model and evaluating it on the [`WebLINX` 1.0](https://mcgill-nlp.github.io/weblinx/) benchmark is available now. 
+* **Modeling**: You can find the detailed instructions in [modeling](modeling/README.md) for training `Llama-3-8B-Web` on the `WebLINX` 1.0 dataset.
 * **Examples**: We provide a few example for using the `webllama` API and models, including web API, end-to-end, and BrowserGym integration. You can find them in [examples](examples/README.md).
-* **App**: We provide a simple Streamlit app for visualizing the results of your model on the `WebLINX` benchmark. You can find the code in [app](app/Results.py).
+* **App**: We provide a simple Streamlit app for visualizing the results of your model on the `WebLINX` 1.0 benchmark. You can find the code in [app](app/Results.py).
 * **Docs**: We provide detailed documentation for the code in [docs](docs/README.md).