Update README.md

README.md

@@ -22,43 +22,7 @@ tags:
 
 # PakMosaic-Small
 
-**Pakistan NLP Suite** · published by [Proxima AI](https://huggingface.co/ProximaAI)
-
-**Status:** research preview · **SOTA-target** · not a factual SOTA claim  
-`SOTA_VERIFIED = false`
-
-This is the finished **~67M** PakMosaic encoder after a **2B-token** serious train on the clean scale mix. It is not PakMosaic-Base-132M (that run is still in progress). It is not a published SOTA model.
-
-## Training result
-
-| item | value |
-| --- | --- |
-| run | `pakmosaic-small_20260925T090924Z` |
-| last step | 2,904,769 |
-| tokens seen | 2,000,000,068 / 2,000,000,000 |
-| last-500 mean MLM loss | 1.958 |
-| last-500 mean masked accuracy | 0.640 |
-| hardware | NVIDIA GeForce RTX 4060 8GB (solo) |
-| objective | masked language modeling, mask prob 0.15 |
-| seq length | 512 |
-| tokenizer | PakMosaic-Tokenizer-32k (`hf_byte_bpe_32k_identity`) |
-
-Last-batch loss jumps by language. The 500-step mean is the number to quote.
-
-## Diagnostic evals (not PakBench)
-
-These are engineering sanity checks from `python -m paknlp_registry encoder evaluate`. They are **not** frozen benchmark scores.
-
-| check | result |
-| --- | ---: |
-| Same Urdu sentence vs spacing/punctuation noise (cosine) | 0.760 |
-| Near paraphrase pair (cosine) | 0.900 |
-| Unrelated pair (cosine) | 0.689 |
-| Urdu vs Pashto mean-pool centroid cosine | 0.890 |
-
-Near > far is the expected direction. Centroid cosine is **not** a language-ID score.
-
-SOTA gate (required PakBench evidence): **failed closed**. Missing frozen evaluation, contamination certificate, multi-language task results, macros, and contemporary baselines. Do not write SOTA on this card until that gate returns true.
+This is the finished ~67M PakMosaic encoder after a **2B-token** serious train on the clean scale mix. 
 
 ## Architecture
 
@@ -72,7 +36,6 @@ Encoder-only MLM: pre-norm, RoPE, GeGLU, full attention, tied embeddings.
 | intermediate | 2048 |
 | vocab | 32,000 |
 
-This is a **custom PakMosaic encoder**, not BERT. Hugging Face Transformers loads it with `trust_remote_code=True` (RoPE + GeGLU + pre-norm live in this repo).
 
 ## How to load
 
@@ -89,37 +52,14 @@ print(fill("یہ ایک <mask> ہے۔"))
 
 `AutoModel` also works (`trust_remote_code=True`) and returns encoder hidden states.
 
-Tokenizer: PakMosaic-Tokenizer-32k / `hf_byte_bpe_32k_identity` (hash `0f55e47920211588e6985172cd71338c8aff6226fe912c9668055b44bacc7ce7`). Frozen Urdu fertility 1.250 vs XLM-R 1.382 is a **tokenizer** metric, not model SOTA.
 
 ## Data
 
 Training mix is Wikimedia plus third-party web crawls used under their original terms. **Only Wikimedia / CC BY-SA is redistributed on the Hub:** [PakMosaic-Wikimedia-v0.2](https://huggingface.co/datasets/ProximaAI/PakMosaic-Wikimedia-v0.2).
 
-FineWeb2, HPLT 2.0, and Common Voice are **not** re-uploaded here. Get those from the original steward.
-
-Languages actually sampled in this run: Pashto, Urdu, Punjabi Shahmukhi, Saraiki, Sindhi, Kashmiri, Gujarati. 66 registered identities are not 66 trained languages.
-
-## Intended use
-
-Research on Pakistan-language representation learning and reproduction of the public Wikimedia subset.
-
-## Out of scope
-
-Production deployment, surveillance, claiming 66-language training, or calling this model SOTA.
-
-## Family
-
-| model | params | this release |
-| --- | ---: | --- |
-| PakMosaic-Micro | ~26.6M | earlier engineering run |
-| **PakMosaic-Small** | **~67M** | **this repo, 2B tokens** |
-| PakMosaic-Base-132M | ~132.5M | training, not published |
-
-Project landing: [ProximaAI/PakMosaic](https://huggingface.co/ProximaAI/PakMosaic)
 
 ## Citation
 
-Cite the Pakistan NLP Suite / PakMosaic repository version until a paper exists.
 
 ## Acknowledgements