Update README

README.md

@@ -1,7 +1,7 @@
 ---
 license: gemma
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-*Gemma-SEA-LION-v4-27B (Base Model) Last updated: 2025-08-18*
+*Gemma-SEA-LION-v4-27B (Base Model) Last updated: 2025-08-20*
 
 ---
 
@@ -9,13 +9,16 @@ license: gemma
 
 <!-- Provide a quick summary of what the model is/does. -->
 
+Last updated: 2025-08-20
+
+
+Gemma-SEA-LION-v4-27B is based on Gemma 3 (which supports over 100 languages) 
+and is a multilingual model which has undergone continued pre-training on approximately **500B** tokens across 11 SEA languages: 
+Bahasa Indonesia, Burmese, Chinese, English, Khmer, Lao, Malay, Tagalog, Tamil, Thai and Vietnamese. 
+
 **SEA-LION** is a collection of Large Language Models (LLMs) which have been pretrained and instruct-tuned 
 for the Southeast Asia (SEA) region.
 
-Gemma-SEA-LION-v4-27B is a multilingual model which has undergone continued pre-training on 
-approximately **500B** tokens across 11 SEA languages: Bahasa Indonesia, Burmese, Chinese, English, 
-Khmer, Lao, Malay, Tagalog, Tamil, Thai and Vietnamese.
-
 
 ## Model Details
 
@@ -26,7 +29,7 @@ Khmer, Lao, Malay, Tagalog, Tamil, Thai and Vietnamese.
 SEA-LION stands for *Southeast Asian Languages In One Network*. 
 
 We performed continued pre-training in English and SEA languages on Gemma 3 27B IT, 
-a decoder model using the Gemma 3 architecture, to create Gemma-SEA-LION-v4-27B.
+a decoder model using the Gemma 3 architecture, to create *Gemma-SEA-LION-v4-27B*.
 
 For tokenization, the model employs the default tokenizer used in Gemma 3 27B IT.
 
@@ -64,7 +67,7 @@ fine-tuning and related security measures. In no event shall the authors be held
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-It is important for users to be aware that our model exhibits certain limitations that warrant consideration. 
+*The model was not tested for robustness against adversarial prompting.* It is important for users to be aware that our model exhibits certain limitations that warrant consideration. 
 Like many LLMs, the model can hallucinate and occasionally generates irrelevant content, 
 introducing fictional elements that are not grounded in the provided context. 
 Users should also exercise caution in interpreting and validating the model's responses 
@@ -201,34 +204,49 @@ We evaluated Gemma-SEA-LION-v4-27B on general language capabilities.
 
 **Testing Data**
 
-General NLP Behaviour
-
-For the evaluation of general language capabilities, we employed the SEA-HELM evaluation benchmark
+For the evaluation of general language capabilities, we employed the [SEA-HELM evaluation benchmark](https://arxiv.org/abs/2502.14301) 
 across a variety of tasks. These tasks include Question Answering (QA), Sentiment Analysis (Sentiment), 
-Toxicity Detection (Toxicity), Translation in both directions (Eng>Lang & Lang>Eng), 
+Toxicity Detection (Toxicity), Metaphor Understanding, Translation in both directions (Eng>Lang & Lang>Eng), 
 Abstractive Summarisation (Abssum), Causal Reasoning (Causal), Natural Language Inference (NLI), 
-and linguistic diagnostics (LINDSEA).
+Linguistic Diagnostics (LINDSEA) and Global-MMLU-Lite.
 
 
 #### Factors
 
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-Our evaluations were set based on task. For all tasks, the model is expected to provide an answer tag 
-from which the answer is automatically extracted. For tasks where options are provided, 
-the answer should comprise one of the pre-defined options. The scores for each task is normalised to account 
-for baseline performance due to random chance.
+All evaluations were run with the model specific generation parameters defined in the model config. 
+Each evaluation comprised of 8 runs with different seeds and the final results were averaged across these runs.
+
+For all tasks, the model was expected to provide an answer tag from which the answer was automatically extracted. 
+For tasks where options were provided, the answer should comprise one of the pre-defined options.
+
+The evaluation was done **five-shot** with native prompts on a sample of 100-1000 instances for each dataset.
 
 
 #### Metrics
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-The evaluation was done **five-shot** with native prompts on a sample of 100-1000 instances for each dataset.
+The following metrics were used:
+| Task                           | Metric                         |
+|---------------------------------|-------------------------------|
+| Sentiment Analysis             | Accuracy                      |
+| Extractive QA (ID, VI, TH, TA) | ChrF++                        |
+| MCQ-QA (TL, MY, MS)            | Accuracy                      |
+| Metaphor                       | Accuracy                      |
+| Abstractive Summarisation      | Rouge-L                       |
+| Translations                   | MetricX-24 score (with reference) |
+| Causal Reasoning               | Accuracy                      |
+| Natural Language Inference     | Accuracy                      |
+| LINDSEA                        | Accuracy                      |
+| Global MMLU Lite               | Accuracy                      |
+| Toxicity Detection             | Accuracy                      |
+
 
 ### Results
 
-For details on Gemma-SEA-LION-v4-27B performance, please refer to the SEA-HELM leaderboard, [Leaderboard results on SEA-HELM](https://leaderboard.sea-lion.ai/).
+Coming soon.
 
 
 #### Summary
@@ -246,12 +264,12 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 - **Hours used:** 214 hrs 
 - **Cloud Provider:** SMC H200
 - **Compute Region:** Singapore
-- **Carbon Emitted:** appx. 35.27 - 98 kg CO2 e
+- **Carbon Emitted:** appx. 98 kg CO2 e
 
 ## More Information
 
 This is the repository for the commercial instruction-tuned model. 
-The model has not been aligned for safety. Developers and users should perform their own safety 
+The model has *not* been aligned for safety. Developers and users should perform their own safety 
 fine-tuning and related security measures. In no event shall the authors be held liable 
 for any claims, damages, or other liabilities arising from the use of the released weights and codes.