Update README.md

README.md

@@ -83,45 +83,6 @@ To build the SFT & DPO data, we utilized various methodologies including:
 - Creating data for sentence and document translation using high-quality parallel corpora mainly from [ELRC-SHARE](https://elrc-share.eu/).
 - Synthetically extracting question-answer pairs and multi-turn dialogues from diverse sources such as Wikipedia, EUR-LEX, Greek School Books, and Kallipos.
 
-# Evaluation
-
-In the table below, we report the scores for our chat evaluation suite which includes:
-- [Greek IFEval](https://huggingface.co/datasets/ilsp/ifeval_greek) (strict)
-- [English IFEval](https://huggingface.co/datasets/google/IFEval) (strict)
-- [Greek MT-Bench](https://huggingface.co/datasets/ilsp/mt-bench-greek) using gpt-4o-2024-08-06 as the judge model.
-- [English MT-Bench](https://huggingface.co/datasets/HuggingFaceH4/mt_bench_prompts) using gpt-4o-2024-08-06 as the judge model.
-
-We can observe that *Llama-Krikri-8B-Instruct exhibits the strongest performance* in instruction following for both Greek and English across all the models we tested. In particular, it surpasses Llama-3.1-8B-Instruct by **+21.7%** and **+7.3%** on the Greek and English IFEval respectively.
-It also exhibits **the strongest chat capabilities in the Greek MT-Bench benchmark** (+0.28 compared to Aya Expanse 8B), while also being very competitive in the English variant of the MT-Bench benchmark.
-
-|       | IFEval EL (strict) | IFEval EN (strict) | MT-Bench EL | MT-Bench EN |
-|---------------- |---------------- |----------------- |------------|------------|
-| Qwen 2.5 7B Instruct | 46.2% | 74.8% | 5.83 | **7.87** |
-| EuroLLM 9B Instruct | 51.3% | 64.5% | 5.98 | 6.27 |
-| Aya Expanse 8B | 50.4% | 62.2% | 7.68 | 6.92 |
-| Meltemi 7B v1.5 Instruct | 32.7% | 41.2% | 6.25 | 5.46 |
-| Llama-3.1-8B Instruct | 45.8% | 75.1% | 6.46 | 7.25 |
-| **Llama-Krikri-8B Instruct** | **67.5%** | **82.4%** | **7.96** | 7.21 |
-
-
-We also used the [Arena-Hard-Auto](https://huggingface.co/datasets/lmarena-ai/arena-hard-auto-v0.1) automatic evaluation tool, as well the translated (and post-edited) version for Greek that is publicly available [here](https://huggingface.co/datasets/ilsp/m-ArenaHard_greek). We report 2 scores for Arena-Hard-Auto:
-- No Style Control: The original version of the benchmark.
-- With Style Control: The benchmark with style control methods for Markdown elements. You can read more about the methodology and technical background in this [blogspot](https://lmsys.org/blog/2024-08-28-style-control/).
-
-Below, we show the scores for the Greek version of Arena-Hard-Auto for various open and closed chat models that were determined using **gpt-4o-2024-08-06 as the judge model** and **gpt-4o-mini-2024-07-18 as the baseline model** (i.e., by default 50% score).
-
-Llama-Krikri-8B Instruct exhibits very strong chat capabilities by scoring **higher than models over 8 times its size** (such as Llama-3.1-70B Instruct) and is also **competitive with closed-source** (e.g., GPT-4o-Mini) and **highly-performant open-source models** (e.g., Gemma 2 27B IT & Aya Expanse 32B). 
-![image/png](arena_hard_el.png)
-
-Below, we show the scores for the original Arena-Hard-Auto dataset for various open and closed chat models. We followed the original methodology by using **gpt-4-1106-preview as the judge model** and **gpt-4-0314 as the baseline model**.
-
-Llama-Krikri-8B Instruct performs very well in the English variant of Arena-Hard-Auto as well, since we can observe that it is **competitive with significantly larger previous-generation LLMs** (such as Qwen 2 72B Instruct) and that it **improves upon Llama-3.1-8B Instruct by +24.5% / +16%** (No style control / With style control). 
-![image/png](arena_hard_en.png)
-
-***Please note** that judge models are biased towards student models trained on distilled data from them. You can read more [here](https://arxiv.org/pdf/2502.01534?).
-
-🚨 **More information on post-training, methodology, and evaluation coming soon.** 🚨
-
 
 # How to use
 
@@ -191,6 +152,46 @@ print(response.choices[0].message.content)
 # ['Ηθική καθήκοντος', 'Μεταμοντέρνα ηθική', 'Συνεπειοκρατική ηθική', 'Ωφελιμιστική ηθική', 'Δεοντολογική ηθική', 'Ηθική αρετών', 'Σχετικιστική ηθική']
 ```
 
+# Evaluation
+
+In the table below, we report the scores for our chat evaluation suite which includes:
+- [Greek IFEval](https://huggingface.co/datasets/ilsp/ifeval_greek) (strict average)
+- [English IFEval](https://huggingface.co/datasets/google/IFEval) (strict average)
+- [Greek MT-Bench](https://huggingface.co/datasets/ilsp/mt-bench-greek) using gpt-4o-2024-08-06 as the judge model.
+- [English MT-Bench](https://huggingface.co/datasets/HuggingFaceH4/mt_bench_prompts) using gpt-4o-2024-08-06 as the judge model.
+
+We can observe that *Llama-Krikri-8B-Instruct exhibits the strongest performance* in instruction following for both Greek and English across all the models we tested. In particular, it surpasses Llama-3.1-8B-Instruct by **+21.7%** and **+7.3%** on the Greek and English IFEval respectively.
+It also exhibits **the strongest chat capabilities in the Greek MT-Bench benchmark** (+0.28 compared to Aya Expanse 8B), while also being very competitive in the English variant of the MT-Bench benchmark.
+
+|       | IFEval EL (strict avg) | IFEval EN (strict avg) | MT-Bench EL | MT-Bench EN |
+|---------------- |---------------- |----------------- |------------|------------|
+| Qwen 2.5 7B Instruct | 46.2% | 74.8% | 5.83 | **7.87** |
+| EuroLLM 9B Instruct | 51.3% | 64.5% | 5.98 | 6.27 |
+| Aya Expanse 8B | 50.4% | 62.2% | 7.68 | 6.92 |
+| Meltemi 7B v1.5 Instruct | 32.7% | 41.2% | 6.25 | 5.46 |
+| Llama-3.1-8B Instruct | 45.8% | 75.1% | 6.46 | 7.25 |
+| **Llama-Krikri-8B Instruct** | **67.5%** | **82.4%** | **7.96** | 7.21 |
+
+
+We also used the [Arena-Hard-Auto](https://huggingface.co/datasets/lmarena-ai/arena-hard-auto-v0.1) automatic evaluation tool, as well the translated (and post-edited) version for Greek that is publicly available [here](https://huggingface.co/datasets/ilsp/m-ArenaHard_greek). We report 2 scores for Arena-Hard-Auto:
+- No Style Control: The original version of the benchmark.
+- With Style Control: The benchmark with style control methods for Markdown elements. You can read more about the methodology and technical background in this [blogspot](https://lmsys.org/blog/2024-08-28-style-control/).
+
+Below, we show the scores for the Greek version of Arena-Hard-Auto for various open and closed chat models that were determined using **gpt-4o-2024-08-06 as the judge model** and **gpt-4o-mini-2024-07-18 as the baseline model** (i.e., by default 50% score).
+
+Llama-Krikri-8B Instruct exhibits very strong chat capabilities by scoring **higher than models over 8 times its size** (such as Llama-3.1-70B Instruct) and is also **competitive with closed-source** (e.g., GPT-4o-Mini) and **highly-performant open-source models** (e.g., Gemma 2 27B IT & Aya Expanse 32B). 
+![image/png](arena_hard_el.png)
+
+Below, we show the scores for the original Arena-Hard-Auto dataset for various open and closed chat models. We followed the original methodology by using **gpt-4-1106-preview as the judge model** and **gpt-4-0314 as the baseline model**.
+
+Llama-Krikri-8B Instruct performs very well in the English variant of Arena-Hard-Auto as well, since we can observe that it is **competitive with significantly larger previous-generation LLMs** (such as Qwen 2 72B Instruct) and that it **improves upon Llama-3.1-8B Instruct by +24.5% / +16%** (No style control / With style control). 
+![image/png](arena_hard_en.png)
+
+***Please note** that judge models are biased towards student models trained on distilled data from them. You can read more [here](https://arxiv.org/pdf/2502.01534?).
+
+🚨 **More information on post-training, methodology, and evaluation coming soon.** 🚨
+
+
 # Acknowledgements
 
 The ILSP team utilized Amazon's cloud computing services, which were made available via GRNET under the [OCRE Cloud framework](https://www.ocre-project.eu/), providing Amazon Web Services for the Greek Academic and Research Community.