Org card: banner, one line per model

README.md

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-# Netra Lab
+<img src="https://huggingface.co/spaces/Netra-Lab/README/resolve/main/assets/banner.png" width="100%" alt="Netra Lab: small models for the Khmer language" />
 
-Netra Lab is an independent, non-commercial research lab that focuses on building small scale model for the Khmer language.
+Netra Lab is an independent, non-commercial research lab that focuses on building small-scale models for the Khmer language.
 
 ## Models
 
-### Reading
-
-**[Netra OCR 1](https://huggingface.co/Netra-Lab/Netra-OCR)** reads printed Khmer and English. A 19M-parameter text-line recognizer trained only on synthetic images, it has a 0.96% character error rate on the KHOB benchmark, against 5.12% for Tesseract 5. It comes with line and layout detection, PDF input and an editor that runs in the browser.<br>
-`pip install netra-ocr` · [GitHub](https://github.com/netra-ai-lab/Netra-OCR) · [Report](https://netra-lab.netlify.app/research/netra-ocr)
-
-### Translation
-
-**[Netra MT 1](https://huggingface.co/Netra-Lab/Netra-NMT)** translates English↔Khmer, both directions in one 77M-parameter model trained from scratch on 220M tokens. On English→Khmer it scores 25.8 spBLEU on ALT, second only to Google Translate and ahead of NLLB-200 1.3B. On Khmer→English it still trails the larger models. About 40 ms per sentence on a desktop CPU.<br>
-`pip install netra-nmt` · [GitHub](https://github.com/netra-ai-lab/Netra-NMT) · [Demo](https://huggingface.co/spaces/Darayut/Netra-NMT) · [Report](https://netra-lab.netlify.app/research/netra-mt)
-
-### Speech
-
-**Sota** transcribes Khmer and English speech, in two sizes pruned from Meta's 975M-parameter Omnilingual ASR. The 204M model has a 1.77% character error rate on the DDD Khmer test set, below its teacher's 2.47%, at a quarter of the compute.<br>
-[sota-large](https://huggingface.co/Darayut/sota-large) · [sota-base](https://huggingface.co/Darayut/sota-base) · [GitHub](https://github.com/netra-ai-lab/SOTA) · [Report](https://netra-lab.netlify.app/research/sota)
-
-### Typing
-
-**AkaraAlpha** turns Khmer typed in the Latin alphabet back into Khmer script. With 78.7K parameters, it has a lower character error rate than a Transformer three times its size, and with a dictionary check its first suggestion is right 74% of the time.<br>
-[GitHub](https://github.com/netra-ai-lab/Romanized-Khmer-to-Khmer-Transliteration) · [Report](https://netra-lab.netlify.app/research/akara-alpha)
+| Model | What it does | Parameters |
+| --- | --- | --- |
+| [Netra OCR 1](https://huggingface.co/Netra-Lab/Netra-OCR) | Reads printed Khmer and English text | 19M |
+| [Netra MT 1](https://huggingface.co/Netra-Lab/Netra-NMT) | Translates between English and Khmer | 77M |
+| [Sota](https://huggingface.co/Darayut/sota-large) · [Sota base](https://huggingface.co/Darayut/sota-base) | Transcribes Khmer and English speech | 204M · 73M |
+| [AkaraAlpha](https://github.com/netra-ai-lab/Romanized-Khmer-to-Khmer-Transliteration) | Turns romanized Khmer into Khmer script | 78.7K |
 
 The training corpora and test sets we built for these models are listed below with the datasets.
 
-
-
 [Website](https://netra-lab.netlify.app/) · [GitHub](https://github.com/netra-ai-lab) · [Get in touch](https://netra-lab.netlify.app/contact)