additional proofreading

README.md

@@ -8,20 +8,22 @@ language:
 - su
 license: gemma
 ---
-# Gemma2 9B CPT Sahabat-AI v1
+# Gemma2 9B CPT Sahabat AI v1
 
-**Sahabat-AI** (Indonesian language for “close friends”) is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for Indonesian language and its various dialects. Sahabat-AI ecosystem is co-initiated by Indonesian tech and telecommunication companies:  GoTo Group and Indosat Ooredoo Hutchison.
+Sahabat AI v1 is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for Indonesian languages.
+This is the card for the Gemma2 9B CPT Sahabat AI v1 base model which has undergone continued pre-training from the base [Gemma2 9B CPT SEA-LIONv3 base](https://huggingface.co/aisingapore/gemma2-9b-cpt-sea-lionv3-base) model.
+
+Sahabat is Indonesian for "Close Friends."
 
-This is the card for the Gemma2 9B CPT Sahabat-AI v1 base model which has undergone continued pre-training from the [Gemma2 9B CPT SEA-Lionv3 base](https://huggingface.co/aisingapore/gemma2-9b-cpt-sea-lionv3-base) model.
 
 ## Model Details
 
 ### Model Description
 
-The continued pre-training data for Gemma2 9B CPT Sahabat-AI v1 base model encompasses approximately 50B tokens.
+The continued pre-training data for Gemma2 9B CPT Sahabat AI v1 base model encompasses approximately 50B tokens.
 
-- **Co-initiated by:**  PT GoTo Gojek Tokopedia Tbk, Indosat Ooredoo Hutchison
 - **Developed by:** PT GoTo Gojek Tokopedia Tbk,  AI Singapore
+- **Funded by:** PT GoTo Gojek Tokopedia Tbk, AI Singapore
 - **Model type:** Decoder
 - **Languages:** English, Indonesian, Javanese, Sundanese
 - **License:** [Gemma Community License](https://ai.google.dev/gemma/terms)
@@ -29,146 +31,57 @@ The continued pre-training data for Gemma2 9B CPT Sahabat-AI v1 base model encom
 For tokenisation, the model employs the default tokenizer used in Gemma-2-9B. The model has a context length of 8192.
 
 ### Benchmark Performance
-We evaluated Gemma2 9B CPT Sahabat-AI v1 base model on general language capabilities.
+We evaluated Gemma2 9B CPT Sahabat AI v1 base model on general language capabilities.
 
 #### General Language Capabilities
 For the evaluation of general language capabilities, we employed the 
 - [SEA HELM (also known as BHASA) evaluation benchmark](https://arxiv.org/abs/2309.06085v2) 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), Abstractive Summarization (Summ), Causal Reasoning (Causal) and Natural Language Inference (NLI).
   - We also added support for Javanese and Sundanese for the BHASA tasks whenever applicable
-- and the common English tasks from the [HuggingFace LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard).
-  - These tasks consist of [IFEval, BBH, Math Lvl 5, GPQA, MuSR, and MMLU-PRO.](https://huggingface.co/docs/leaderboards/open_llm_leaderboard/about)
-  - **Caveat**: Our results differ from the HuggingFace LLM Leaderboard because we have used [VLLM](https://docs.vllm.ai/en/latest/) as our inference platform. VLLM caps the context size at **4096 tokens** while HuggingFace was set to **8192 tokens**.
+- [IndoMMLU](https://arxiv.org/pdf/2310.04928)
+  - These tasks include examination questions on Humanities, Indonesian language, Local languages and cultures, Social science and STEM across primary, middle, and high school levels.
+- and the well known [English MMLU](https://arxiv.org/pdf/2009.03300)
 
 Note: SEA HELM is implemented using prompts to elicit answers in a strict format. 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.
 
 The evaluation was done **five-shot** with native prompts on a sample of 100-1000 instances for each dataset.
 
-#### Results
-
-#### SEA HELM (also known as BHASA)
-<table style="border-collapse: collapse; width: 100%; font-size: 10px">
-  <tr>
-    <th style="border: 2px solid black; padding: 8px; font-weight: bold;">Language / Model Name [Base]</th>
-    <th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
-    <th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
-    <th style="border: 1px solid gray; padding: 8px;">Llama-3-8B</th>
-    <th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
-    <th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
-    <th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
-    <th style="border: 1px solid gray; padding: 8px;">sea-lionv3-9B</th>
-    <th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-8B</th>
-    <th style="border: 2px solid black; padding: 8px;">sahabatai-v1-9B</th>
-  </tr>
-  <tr>
-    <td style="border: 2px solid black; padding: 8px; font-weight: bold;">Overall (Bahasa Indonesia + Javanese + Sundanese)</td>
-    <td style="border: 1px solid gray; padding: 8px;">42.776</td>
-    <td style="border: 1px solid gray; padding: 8px;">46.245</td>
-    <td style="border: 1px solid gray; padding: 8px;">49.160</td>
-    <td style="border: 1px solid gray; padding: 8px;">49.577</td>
-    <td style="border: 1px solid gray; padding: 8px;">48.602</td>
-    <td style="border: 1px solid gray; padding: 8px;">58.972</td>
-    <td style="border: 1px solid gray; padding: 8px;">60.913</td>
-    <td style="border: 1px solid gray; padding: 8px;">59.437</td>
-    <td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">64.123</td>
-  </tr>
-  <tr>
-    <td style="border: 2px solid black; padding: 8px; font-weight: bold;">Bahasa Indonesia</td>
-    <td style="border: 1px solid gray; padding: 8px;">49.341</td>
-    <td style="border: 1px solid gray; padding: 8px;">55.913</td>
-    <td style="border: 1px solid gray; padding: 8px;">47.865</td>
-    <td style="border: 1px solid gray; padding: 8px;">48.110</td>
-    <td style="border: 1px solid gray; padding: 8px;">49.154</td>
-    <td style="border: 1px solid gray; padding: 8px;">58.572</td>
-    <td style="border: 1px solid gray; padding: 8px; background-color: lightgreen;">62.437</td>
-    <td style="border: 1px solid gray; padding: 8px;">53.454</td>
-    <td style="border: 2px solid black; padding: 8px;">60.040</td>
-  </tr>
-  <tr>
-    <td style="border: 2px solid black; padding: 8px; font-weight: bold;">Javanese</td>
-    <td style="border: 1px solid gray; padding: 8px;">42.774</td>
-    <td style="border: 1px solid gray; padding: 8px;">45.917</td>
-    <td style="border: 1px solid gray; padding: 8px;">54.627</td>
-    <td style="border: 1px solid gray; padding: 8px;">55.215</td>
-    <td style="border: 1px solid gray; padding: 8px;">52.728</td>
-    <td style="border: 1px solid gray; padding: 8px;">63.760</td>
-    <td style="border: 1px solid gray; padding: 8px;">63.363</td>
-    <td style="border: 1px solid gray; padding: 8px;">65.048</td>
-    <td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">69.882</td>
-  </tr>
-  <tr>
-    <td style="border: 2px solid black; padding: 8px; font-weight: bold;">Sundanese</td>
-    <td style="border: 1px solid gray; padding: 8px;">36.213</td>
-    <td style="border: 1px solid gray; padding: 8px;">36.905</td>
-    <td style="border: 1px solid gray; padding: 8px;">44.988</td>
-    <td style="border: 1px solid gray; padding: 8px;">45.407</td>
-    <td style="border: 1px solid gray; padding: 8px;">43.925</td>
-    <td style="border: 1px solid gray; padding: 8px;">54.583</td>
-    <td style="border: 1px solid gray; padding: 8px;">56.939</td>
-    <td style="border: 1px solid gray; padding: 8px;">59.809</td>
-    <td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">62.446</td>
-  </tr>
-</table>
-
-#### English Results
-<table style="border-collapse: collapse; width: 100%; font-size: 10px">
-  <tr>
-    <th style="border: 1px solid gray; padding: 8px;">Model Name [BASE]</th>
-    <th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
-    <th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
-    <th style="border: 1px solid gray; padding: 8px;">Llama-3-8B</th>
-    <th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
-    <th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
-    <th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
-    <th style="border: 1px solid gray; padding: 8px;">sea-lionv3-9B</th>
-    <th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-8B</th>
-    <th style="border: 2px solid black; padding: 8px;">sahabatai-v1-9B</th>
-  </tr>
-  <tr>
-    <td style="border: 1px solid gray; padding: 8px; font-weight: bold;">Average</td>
-    <td style="border: 1px solid gray; padding: 8px;">23.68</td>
-    <td style="border: 1px solid gray; padding: 8px; background-color: lightgreen;">24.65</td>
-    <td style="border: 1px solid gray; padding: 8px;">13.56</td>
-    <td style="border: 1px solid gray; padding: 8px;">13.69</td>
-    <td style="border: 1px solid gray; padding: 8px;">12.77</td>
-    <td style="border: 1px solid gray; padding: 8px;">13.34</td>
-    <td style="border: 1px solid gray; padding: 8px;">21.99</td>
-    <td style="border: 1px solid gray; padding: 8px;">13.92</td>
-    <td style="border: 2px solid black; padding: 8px;">19.62</td>
-  </tr>
-</table>
-
 
 ## Training Details
 
 ### Data
 
-Gemma2 9B CPT Sahabat-AI v1 base model was continued pre-trained on 50B tokens of the following data:
+Gemma2 9B CPT Sahabat AI v1 base model was continued pre-trained on 50B tokens of the following data:
 
 | Data Source                           | Unique Tokens (B) | Multiplier | Total Tokens (B) | Percentage (%)|
 |---------------------------------------|:-----------------:|:----------:|:----------------:|:-------------:|
-| Dolma Refined Web                     | 9.5               | 1          | 9.5              | 18.7          |
-| Dolma arXiv                           | 0.6               | 1          | 0.6              | 1.18          |
-| Stack V2                              | 5.5               | 1          | 5.5              | 10.85         |
-| Dolma Semantic Scholar                | 1.2               | 1          | 1.2              | 2.37          |
-| Dolma Reddit                          | 1.7               | 1          | 1.7              | 3.36          |
-| Dolma Pes2o                           | 1.2               | 1          | 1.2              | 2.37          |
-| Wiki* + News* - Indonesian            | 1.0               | 1          | 1.0              | 1.97          |
-| SEA-LION Pile - Indonesian            | 27.0              | 1          | 27.0             | 53.3          |
-| JV Pile - Javanese                    | 0.92              | 1.6        | 1.5              | 3.0        	|
-| SU Pile - Sundanese                   | 0.39              | 3.8        | 1.5              | 3.0        	|
+| Dolma Refined Web                     | 9.5               | 1          | 9.5              | 17.36         |
+| Dolma arXiv                           | 0.6               | 1          | 0.6              | 1.10          |
+| Dolma Star Coder                      | 5.5               | 1          | 5.5              | 10.05         |
+| Dolma Semantic Scholar                | 1.2               | 1          | 1.2              | 2.19          |
+| Dolma Reddit                          | 1.7               | 1          | 1.7              | 3.11          |
+| Dolma C4                              | 1.5               | 1          | 1.4              | 2.56          |
+| Wiki* + News* - Indonesian            | 1.3               | 4          | 5.2              | 9.50          |
+| SEA-LION Pile - Indonesian            | 27.0              | 1          | 27.0             | 49.34         |
+| SEA-LION Pile - Javanese              | 0.5               | 1.5        | 0.75             | 1.37        	|
+| CC 100 - Javanese                     | 0.05              | 1.5        | 0.075            | 0.14        	|
+| HPLT - Javanese                       | 0.3               | 1.5        | 0.45             | 0.82        	|
+| SEA-LION Pile - Sundanese             | 0.2               | 3.6        | 0.75             | 1.37        	|
+| CC 100 - Sundanese                    | 0.02              | 3.6        | 0.075            | 0.14        	|
+| HPLT - Sundanese                      | 0.16              | 3.6        | 0.45             | 0.82        	|
+| Others (Javanese, Sundanese)          | 0.034             | 2.2        | 0.076            | 0.14       	|
 
 Note: 
 - All token counts are counted using Gemma2 tokenizer
 - Wiki* sources includes Wikipedia, Wiki Books, Wiki Source, Wiki Voyage and Fandom Wiki
-- News* sources includes VOA, Global Voices
+- News* sources includes VOA, Global Voices, MediaCorp
 
 ### Infrastructure
 
-Gemma2 9B CPT Sahabat-AI v1 was trained using [MosaicML Composer](https://github.com/mosaicml/composer)
+Gemma2 9B CPT Sahabat AI v1 was trained using [MosaicML Composer](https://github.com/mosaicml/composer)
 on the following hardware:
 
-| Training Details     | Gemma2 9B CPT Sahabat-AI v1|
+| Training Details     | Gemma2 9B CPT Sahabat AI v1|
 |----------------------|:--------------------------:|
 | Nvidia H100 80GB GPU |        32                  |
 | Training Duration    |        7 days              |
@@ -176,7 +89,7 @@ on the following hardware:
 
 ### Configuration
 
-| HyperParameter    | Gemma2 9B CPT Sahabat-AI v1|
+| HyperParameter    | Gemma2 9B CPT Sahabat AI v1|
 |-------------------|:--------------------------:|
 | Precision         | bfloat16                   |
 | Optimizer         | decoupled_adamw            |
@@ -185,30 +98,12 @@ on the following hardware:
 | Global Batch Size | 256                        |
 | Micro Batch Size  | 1                          |
 
-## Call for Collaboration 
-
-Sahabat-AI (Indonesian language for “close friends”) a **local open source Large Language Model (LLM) ecosystem in Indonesian language**, co-initiated by Indonesian tech and telecommunication companies:  GoTo Group and Indosat Ooredoo Hutchison. 
-Sahabat-AI ecosystem aims to empower Indonesians who want to develop AI-based services and applications using Bahasa Indonesia and its various local dialects. 
-
-We are supported by research centers and global tech experts such as AI Singapore and Tech Mahendra to train the model to gain general language understanding.
-
-We also collaborate with key top Indonesia universities such as University of Indonesia, Gadjah Mada University, Bogor Institute of Agriculture, Bandung Institute of Technology, including top Indonesia media groups, such as Kompas Gramedia Group and Republika to train and enrich the model in Bahasa Indonesia, ensuring optimum provision of local context and cultural relevance.
 
-We would like to invite **researchers, developers, and language enthusiasts** to actively contribute to the enhancement and expansion of Sahabat-AI. 
-Your collaborations can involve:
-- Identifying and reporting technical issues
-- Sharing pre-training, instruction, and preference data
-- Improving documentation usability
-- Proposing and implementing new model evaluation tasks and metrics 
-
-Join us in shaping the future of Sahabat-AI by sharing your expertise and insights to make these models more accessible, accurate, and versatile.
-
-You can contribute your ideas through [this form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)
-
-## The Development Team (in ascending alphabetical order)
+## The Team (by ascending alphabetical order)
 
 ### AI Singapore
 Chan Adwin<br>
+Chau Shiau Ching<br>
 Cheng Nicholas<br>
 Choa Esther<br>
 Huang Yuli<br>
@@ -239,7 +134,6 @@ Yong Xianbin<br>
 
 ### PT GoTo Gojek Tokopedia Tbk
 Anissa Dininta<br>
-Chau Shiau Ching<br>
 Choiri Hendra Hadhil<br>
 Goel Priyank<br>
 Saini Ajay Kumar<br>
@@ -249,15 +143,16 @@ Tep Kilian Rithi<br>
 Tiwari Anupam<br>
 Widjojo Daniel<br>
 
+<!--
 ## Acknowledgements
 
 AI Singapore is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore.
+Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore. -->
 
-Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.
 
 ## Contact
 
-For more info, please contact us using this [Sahabat-AI Inquiry Form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)
+For more info, please contact us using this [Sahabat Inquiry Form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)
 
 
 ## Disclaimer
@@ -265,4 +160,21 @@ For more info, please contact us using this [Sahabat-AI Inquiry Form.](https://d
 This is the repository for the base model.
 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 claim, damages, or other liability arising from the use of the released weights and codes.
+In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.
+
+
+## References
+### IndoMMLU Reference
+
+```bibtex
+@inproceedings{koto-etal-2023-indommlu,
+    title = "Large Language Models Only Pass Primary School Exams in {I}ndonesia: A Comprehensive Test on {I}ndo{MMLU}",
+    author = "Fajri Koto and Nurul Aisyah and Haonan Li and Timothy Baldwin",
+    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    month = December,
+    year = "2023",
+    address = "Singapore",
+    publisher = "Association for Computational Linguistics",
+}
+}
+```