final leaderboard update - Adithya S K

app.py

@@ -56,7 +56,7 @@ def main():
         if st.button("Refresh", type="primary"):
             data = get_data()
     
-    Leaderboard_tab, About_tab ,FAQ_tab, Submit_tab = st.tabs(["🏅 Leaderboard", "📝 About" , "❗FAQ","🚀 Submit"])
+    Leaderboard_tab, Release_tab,  About_tab ,FAQ_tab, Submit_tab = st.tabs(["🏅 Leaderboard", "(α) Release" ,"📝 About" , "❗FAQ","🚀 Submit"])
     
     with Leaderboard_tab:
         data = get_data()
@@ -135,7 +135,7 @@ def main():
         with col2:
             language_options = st.multiselect(
                 'Pick Languages',
-                ['kannada', 'hindi', 'tamil', 'telegu','gujarathi','marathi','malayalam',"english"],['kannada', 'hindi', 'tamil', 'telegu','gujarathi','marathi','malayalam',"english"])
+                ['kannada', 'hindi', 'tamil', 'telegu','gujarati','marathi','malayalam',"english"],['kannada', 'hindi', 'tamil', 'telegu','gujarati','marathi','malayalam',"english"])
         if on:
             # Loop through each selected language
             for language in language_options:
@@ -217,7 +217,82 @@ def main():
                 compare_df.index += 1
                 st.dataframe(compare_df, use_container_width=True)
                 
+    with Release_tab:
+        st.markdown(
+    """
+    **Date: April 5th, 2024**
 
+the alpha release of the **Indic LLM Leaderboard** and **Indic Eval**.
+
+The Indic LLM Leaderboard is an evolving platform, aiming to streamline evaluations for Language Model (LLM) models tailored to Indic languages. While this **alpha release is far from perfect**, it signifies a crucial initial step towards establishing evaluation standards within the community.
+
+### Features:
+
+As of this release, the following base models have been evaluated in using the different datasets and benchmarks integrated into the platform:
+
+- `meta meta-llama/Llama-2-7b-hf`
+- `google/gemma-7b`
+
+Tasks incorporated into the platform:
+
+- `ARC-Easy:{language}`
+- `ARC-Challenge:{language}`
+- `Hellaswag:{language}`
+
+For evaluation purposes, each task includes 5-shot prompting. Further experimentation will determine the most optimal balance between evaluation time and accuracy.
+
+### Datasets:
+
+Datasets utilized for evaluation are accessible via the following link: [Indic LLM Leaderboard Eval Suite](https://huggingface.co/collections/Cognitive-Lab/indic-llm-leaderboard-eval-suite-660ac4818695a785edee4e6f)
+
+### Rationale for Alpha Release:
+
+The decision to label this release as alpha stems from the realization that extensive testing and experimentation are necessary. Key considerations include:
+
+- Selection of appropriate metrics for evaluation
+- Determination of the optimal few-shot learning parameters
+- Establishment of the ideal number of evaluation samples within the dataset
+
+### Collaborative Effort:
+
+To foster collaboration and discussion surrounding evaluations, a [WhatsApp group](https://chat.whatsapp.com/CUb6eS50lX2JHX2D4j13d1) is being established.
+
+and we can also connect on Hugging faces discord [indic_llm channel](https://discord.com/channels/879548962464493619/1189605147068858408)
+
+### Roadmap for Next Release:
+
+Anticipate the following enhancements in the upcoming release:
+
+- Enhanced testing and accountability mechanisms
+- A refined version of the leaderboard
+- Defined benchmarks and standardized datasets
+- Bilingual evaluation support
+- Expansion of supported models
+- Implementation of more secure interaction mechanisms
+- Addition of support for additional languages
+
+### Benchmarks to be added/tested 
+
+- [ ] Boolq
+- [ ] MMLU
+- [ ] Translation - [IN22-Gen](https://huggingface.co/datasets/ai4bharat/IN22-Gen), [Flores](https://huggingface.co/datasets/facebook/flores)
+- [ ] Generation - [ai4bharat/IndicSentiment](https://huggingface.co/datasets/ai4bharat/IndicSentiment), etc..
+
+Upcoming Implementations
+
+- [ ] Support to add VLLM for faster evaluation and inference
+- [ ] Add support for onboard evaluation just like OpenLLM Leaderboard
+
+## Conclusion:
+
+The alpha release of the Indic LLM Leaderboard and Indic Eval signifies a significant milestone in the pursuit of standardized evaluations for Indic language models. We invite contributions and feedback from the community to further enhance and refine these tools.
+
+For more information and updates, visit [Indic LLM Leaderboard](https://huggingface.co/spaces/Cognitive-Lab/indic_llm_leaderboard) and [Indic Eval](https://github.com/adithya-s-k/indic_eval).
+
+Thank you for your interest and support.
+
+    """
+        )
 
     # About tab
     with About_tab:
@@ -243,12 +318,6 @@ After releasing [Amabri, a 7b parameter English-Kannada bilingual LLM](https://w
 - [Indic-Eval](https://github.com/adithya-s-k/indic_eval): A lightweight evaluation suite tailored specifically for assessing Indic LLMs across a diverse range of tasks, aiding in performance assessment and comparison within the Indian language context.
 - [Indic LLM Leaderboard](https://huggingface.co/spaces/Cognitive-Lab/indic_llm_leaderboard): Utilizes the [indic_eval](https://github.com/adithya-s-k/indic_eval) evaluation framework, incorporating state-of-the-art translated benchmarks like ARC, Hellaswag, MMLU, among others. Supporting seven Indic languages, it offers a comprehensive platform for assessing model performance and comparing results within the Indic language modeling landscape.
 
-## **Upcoming implementations**
-
-- [ ] Support to add VLLM for faster evaluation and inference
-- [ ] SkyPilot installation to quickly run indic_eval on any cloud provider
-- [ ] Add support for onboard evaluation just like OpenLLM Leaderboard
-
 **Contribute**
 
 All the projects are completely open source with different licenses, so anyone can contribute.