Update README.md

README.md

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 # AIDO.RNA 1.6B
 
-AIDO.RNA is an RNA foundation model trained on 42 million non-coding RNA sequences at single-nucleotide resolution. It achieves state-of-the-art performance on a comprehensive set of tasks, including RNA secondary structure prediction, mRNA-related tasks, RNA function prediction tasks, and RNA inverse folding.
+AIDO.RNA is a 1.6B parameter RNA foundation model trained on 42 million non-coding RNA sequences at single-nucleotide resolution. It achieves state-of-the-art performance on a comprehensive set of tasks, including RNA secondary structure prediction, mRNA-related tasks, RNA function prediction tasks, and RNA inverse folding.
 
 <img src="https://cdn-uploads.huggingface.co/production/uploads/63008d4bc1e149ceaff724a3/mNqn5SKQFHxSby3E2dosE.png" alt="description" style="width:80%; height:auto;">
 
+## Model architectural details
+TODO
 
-## How to Use
+## Pre-training data
+TODO
 
-### Sequence-level Regression
+## Downstream evaluation
+TODO
 
+## How to Use
+Build any downstream models from this backbone
+
+### Get RNA sequence embedding
+```
+from genbio_finetune.tasks import Embed
+model = Embed.from_config({"model.backbone": "rnafm"})
+collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
+embedding = model(collated_batch)
+print(embedding.shape)
+print(embedding)
+```
 
-### Sequence-level Classification
+### Sequence-level classification
 ```
 import torch
 from genbio_finetune.tasks import SequenceClassification
-from genbio_finetune.models import MLPPoolAdapter
-model = SequenceClassification.from_config({"model.backbone": "rnafm",
-                                            "model.n_classes": 2,
-                                            "model.adapter": MLPPoolAdapter,
-                                            })
+model = SequenceClassification.from_config({"model.backbone": "rnafm", "model.n_classes": 2})
 collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
 logits = model(collated_batch)
 print(logits)
 print(torch.argmax(logits, dim=-1))
 ```
 
-### Pairwise Token-level Classification
-TODO
+### Token-level classification
+```
+import torch
+from genbio_finetune.tasks import TokenClassification
+model = TokenClassification.from_config({"model.backbone": "rnafm", "model.n_classes": 3})
+collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
+logits = model(collated_batch)
+print(logits)
+print(torch.argmax(logits, dim=-1))
+```
 
 
+### Pairwise token-level classification
+@Sazan TODO
+
+
+### Sequence-level regression
+```
+from genbio_finetune.tasks import SequenceRegression
+model = SequenceRegression.from_config({"model.backbone": "rnafm"})
+collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
+logits = model(collated_batch)
+print(logits)
+```
+
+Or use our one-liner CLI to finetune or evaluate any of the above!
+```
+gbft fit --model SequenceClassification --model.backbone rnafm --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
+gbft test --model SequenceClassification --model.backbone rnafm --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
+```
+
+For more information, visit: [Model Generator](https://github.com/genbio-ai/test)
+
 ## Citation
-TODO
+Please cite AIDO.RNA using the following BibTeX code:
+
+@inproceedings{ellington2024accurate,
+title={Accurate and General {DNA} Representations Emerge from Genome Foundation Models at Scale},
+author={Caleb Ellington, Ning Sun, Nicholas Ho, Tianhua Tao, Sazan Mahbub, Yonghao Zhuang, Hongyi Wang, Eric P. Xing, Le Song},
+booktitle={NeurIPS 2024 Workshop on AI for New Drug Modalities},
+year={2024}
+}
+
+## License
+@Hongyi TODO
+
+