Update README.md

README.md

@@ -1,7 +1,4 @@
-Geo-Sign 
-
 ---
-license: cc-by-nc-4.0
 library_name: transformers
 license: mit
 model_name: Geo-Sign (Hyperbolic-Token)
@@ -26,6 +23,10 @@ task:
 **Paper**: *Geo-Sign: Hyperbolic Contrastive Regularisation for Geometrically Aware Sign-Language Translation*  
 Edward Fish, Richard Bowden, CVSSP – University of Surrey (arXiv:2506.00129, May 2025)  
 **Code**: <https://github.com/ed-fish/geo-sign>
+**Paper** <https://arxiv.org/pdf/2506.00129v1>
+
+## Code Use
+Download the weights and data labels from the files section of this repo and add them to the github repository <https://github.com/ed-fish/geo-sign> under the correct folders (data, checkpoints).
 
 ## TL;DR  
 Geo-Sign projects pose-based sign-language features into a learnable **Poincaré ball** and aligns them with text embeddings via a geometric contrastive loss.  
@@ -33,7 +34,6 @@ Compared with the strong Uni-Sign pose baseline, Geo-Sign boosts BLEU-4 by **+1.
 
 ## Model Details
 | | |
-|---|---|
 | **Backbone** | Four part-specific **ST-GCNs** (body / L-hand / R-hand / face) feeding an mT5-Base decoder |
 | **Hyperbolic branch** | • Learnable curvature \(c\) (init 1.5) • 256-D Poincaré embeddings • Weighted Fréchet-mean pooling (global) or Token-level hyperbolic attention (this checkpoint = **Token**) |
 | **Train data** | Pre-trained pose encoder on **CSL-News** (1 985 h) then fine-tuned 40 epochs on **CSL-Daily** (20 k videos) |
@@ -44,3 +44,34 @@ Compared with the strong Uni-Sign pose baseline, Geo-Sign boosts BLEU-4 by **+1.
 ## Intended Uses & Scope
 * **Primary** – Sign-language-to-text translation research, especially for resource-constrained or privacy-sensitive settings where RGB video is unavailable.  
 * **Out-of-scope** – Real-time production deployments without reliable pose estimation, medical or legal interpretations, or languages beyond datasets the model was trained on.
+
+## Evaluation
+
+| Dataset          | Modality | BLEU-4 ↑ | ROUGE-L ↑ |
+|------------------|----------|----------|-----------|
+| CSL-Daily (test) | Pose-only | **27.42** | **57.95** |
+
+Geo-Sign outperforms all previous gloss-free pose-only methods and rivals many RGB- or gloss-based systems.
+
+---
+
+## Limitations & Ethical Considerations
+
+* **Pose-estimation dependency** – Errors in upstream key-points propagate to the translation.  
+* **Training latency** – Hyperbolic operations slow training (~4–6 ×) but add **no** cost at inference.  
+* **Generalisation** – Evaluated only on Chinese Sign Language; other sign languages are not guaranteed.  
+* **Mis-translation risk** – Automatic SLT can mis-communicate; keep a human in the loop for critical use cases.  
+* **Biases** – CSL-Daily is domain-specific (news/TV); outputs may reflect that linguistic style.
+
+---
+
+## Citation
+
+```bibtex
+@article{fish2025geo,
+  title={Geo-Sign: Hyperbolic Contrastive Regularisation for Geometrically Aware Sign Language Translation},
+  author={Fish, Edward and Bowden, Richard},
+  journal={arXiv preprint arXiv:2506.00129},
+  year={2025}
+}```
+