Update README.md

README.md

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 ---
 tags:
-- model_hub_mixin
+- drug-discovery
+- ibm
+- mammal
+- pytorch
+- TCR
+- epitope
+- affinity
+- safetensors
+- biomed-multi-alignment
+license: apache-2.0
+library_name: biomed-multi-alignment
+base_model:
+- ibm/biomed.omics.bl.sm.ma-ted-458m
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+T-cell receptor (TCR) binding to immunogenic peptides (epitopes) presented by major
+histocompatibility complex (MHC) molecules is a critical mechanism in the adaptive
+immune system, essential for antigen recognition and triggering immune responses.
+The T-cell receptor (TCR) repertoire exhibits considerable diversity, consisting of an
+α-chain and a β-chain that function together to enable T cells to recognize a wide
+array of epitopes. The β-chain is especially significant, as it is crucial for the early
+stages of T-cell development and possesses greater variability, which enhances the
+TCR’s capacity to identify diverse pathogens effectively. However, understanding the
+specific interactions between TCRs and epitopes remains a significant challenge due
+to the vast variability in TCR sequences. Accurate prediction of TCR-peptide binding
+from sequence data could revolutionize immunology by offering deeper insights
+into a patient’s immune status and disease history. This capability holds potential
+applications in personalized immunotherapy, early diagnosis, and the treatment of
+diseases such as cancer and autoimmune disorders. In silico tools designed to model
+TCR-peptide interactions could also facilitate the study of therapeutic T-cell efficacy
+and assess cross-reactivity risks, presenting a transformative opportunity for precision
+medicine.
+
+The benchmark defined in: https://academic.oup.com/bioinformatics/article/37/Supplement_1/i237/6319659?login=false
+Data retrieved from: https://tdcommons.ai/multi_pred_tasks/tcrepitope
+
+## Model Summary
+
+- **Developers:** IBM Research
+- **GitHub Repository:** https://github.com/BiomedSciAI/biomed-multi-alignment
+- **Paper:** https://arxiv.org/abs/2410.22367
+- **Release Date**: Oct 28th, 2024
+- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
+
+## Usage
+
+Using `ibm/biomed.omics.bl.sm.ma-ted-458m.tcr_epitope_bind` requires installing https://github.com/BiomedSciAI/biomed-multi-alignment
+
+```
+pip install git+https://github.com/BiomedSciAI/biomed-multi-alignment.git
+```
+
+A simple example for a task already supported by `ibm/biomed.omics.bl.sm.ma-ted-458m.tcr_epitope_bind`:
+
+See our detailed example at: on `https://github.com/BiomedSciAI/biomed-multi-alignment` 
+
+
+## Citation
+
+If you found our work useful, please consider giving a star to the repo and cite our paper:
+```
+@misc{shoshan2024mammalmolecularaligned,
+      title={MAMMAL -- Molecular Aligned Multi-Modal Architecture and Language}, 
+      author={Yoel Shoshan and Moshiko Raboh and Michal Ozery-Flato and Vadim Ratner and Alex Golts and Jeffrey K. Weber and Ella Barkan and Simona Rabinovici-Cohen and Sagi Polaczek and Ido Amos and Ben Shapira and Liam Hazan and Matan Ninio and Sivan Ravid and Michael M. Danziger and Joseph A. Morrone and Parthasarathy Suryanarayanan and Michal Rosen-Zvi and Efrat Hexter},
+      year={2024},
+      eprint={2410.22367},
+      archivePrefix={arXiv},
+      primaryClass={q-bio.QM},
+      url={https://arxiv.org/abs/2410.22367}, 
+}
+```