update readme

README.md

@@ -4,15 +4,15 @@ license: bsd-3-clause
 
 # HyenaDNA
 
-Welcome! HyenaDNA is a genomic foundation model pretrained on the human reference genome with sequence lengths of up to **1 million tokens** at **single nucleotide resolution**.
+Welcome! HyenaDNA is a long-range genomic foundation model pretrained on context lengths of up to **1 million tokens** at **single nucleotide resolution**. 
 
 See below for an [overview](#model) of the model and training. Better yet, check out these resources.
 
-**Checkout our other resources:**  
+**Resources:**  
 
-- [arxiv](https://arxiv.org/abs/2302.10866) (placeholder)  
-- [blog](https://hazyresearch.stanford.edu/blog/2023-03-07-hyena) (placeholder)  
-- [colab](https://colab.research.google.com/drive/1wyVEQd4R3HYLTUOXEEQmp_I8aNC_aLhL#scrollTo=dgkZggOetkbR)
+- [arxiv](https://arxiv.org/abs/2306.15794)  
+- [blog](https://hazyresearch.stanford.edu/blog/2023-06-29-hyena-dna)  
+- [colab](https://colab.research.google.com/drive/1wyVEQd4R3HYLTUOXEEQmp_I8aNC_aLhL?usp=sharing)
 - [github](https://github.com/HazyResearch/hyena-dna)
 
 
@@ -28,9 +28,9 @@ See below for an [overview](#model) of the model and training. Better yet, check
 ### Sample snippet
 
 
-This code example lets you select which pretrained model to load from HuggingFace and do inference to get embeddings.
+This code example lets you select which pretrained model to load from HuggingFace, perform inference and get embeddings.
 
-See the `huggingface.py` script in the main [github](https://github.com/HazyResearch/hyena-dna), or the [colab](https://colab.research.google.com/drive/1wyVEQd4R3HYLTUOXEEQmp_I8aNC_aLhL#scrollTo=dgkZggOetkbR) for these classes.
+See the [colab](https://colab.research.google.com/drive/1wyVEQd4R3HYLTUOXEEQmp_I8aNC_aLhL?usp=sharing) for these classes, or the ['huggingface.py'](https://github.com/HazyResearch/hyena-dna/blob/main/huggingface.py) script in the main [github](https://github.com/HazyResearch/hyena-dna).
 
 
 ```python
@@ -44,7 +44,7 @@ model = HyenaDNAPreTrainedModel.from_pretrained(
     pretrained_model_name,
 )
 
-# create tokenizer
+# create tokenizer, no training involved :)
 tokenizer = CharacterTokenizer(
     characters=['A', 'C', 'G', 'T', 'N'],  # add DNA characters
     model_max_length=max_length,
@@ -70,16 +70,16 @@ print(embeddings.shape)  # embeddings here!
 
 ### How to use pretrained weights
 
-- [colab](https://colab.research.google.com/drive/1wyVEQd4R3HYLTUOXEEQmp_I8aNC_aLhL#scrollTo=dgkZggOetkbR)  
+- [colab](https://colab.research.google.com/drive/1wyVEQd4R3HYLTUOXEEQmp_I8aNC_aLhL?usp=sharing)  
 
-The colab is the easiest entry point, you can finetune a small model, and do inference on DNA sequences up to 450k on the free tier (T4 GPU), and up to 1 million on the paid tier (A100). It handles all the HuggingFace integration for you, so it's helpful to see how.
+The colab is the easiest entry point, you can finetune a small model, and do inference on DNA sequences up to 450k on the free tier (T4 GPU), and up to 1 million on the paid tier (A100). It handles all the HuggingFace integration for you, so it's helpful to see this example first.
 
 - [github](https://github.com/HazyResearch/hyena-dna)
 
-Otherwise, checkout of the main HyenaDNA repo for how to load weights into Pytorch Lightning. We use Pytorch Lightning for pretraining and fine-tuning most of our models. If you want to use our actual pretraining code, you can clone this HuggingFace repo to download the actual weights.ckpt, and then pass it.
+Otherwise, checkout of the main HyenaDNA repo for how to load weights into Pytorch Lightning. We use Pytorch Lightning for pretraining and fine-tuning all of our models. If you want to use our actual pretraining code, you can clone this HuggingFace repo to download the actual weights.ckpt, and then pass it to Pytorch Lightning via command line or config. See the [github](https://github.com/HazyResearch/hyena-dna) README for how to do all that.
 
 
-If you want a standalone version that's easy to port into your own code, we have that and a HuggingFace example in the repo too, under `huggingface.py`.
+If you want a standalone version that's easy to port into your own code (and not tied to our repo or Pytorch Lightning), we have that and a HuggingFace example in ['huggingface.py'](https://github.com/HazyResearch/hyena-dna/blob/main/huggingface.py) too.
 
 
 ## Model & Training Overview
@@ -96,11 +96,11 @@ We pretrain using next token (nucleotide) prediction on the human reference geno
 HyenaDNA sets new SotA on 23 downstream tasks including predicting regulatory elements, chromatin profiles, and species classification. We also explore what new capabilities open up with long context in genomics, including the first use of in-context learning with soft prompt tuneable tokens and instruction fine-tuning.
 
 
-Check out our [blog](https://hazyresearch.stanford.edu/blog/2023-03-07-hyena) for more details on HyenaDNA!
+Check out our [blog](https://hazyresearch.stanford.edu/blog/2023-06-29-hyena-dna) for more details on HyenaDNA!
 
 ### Authors
 
-Eric Nguyen*, Michael Poli*, Marjan Faizi*, Armin Thomas, Callum Birch Sykes, Michael Wornow, Stefano Massaroli, Aman Patel, Clayton Rabideau, Yoshua Bengio, Stefano Ermon, Stephen Baccus, Chris Re.
+Eric Nguyen*, Michael Poli*, Marjan Faizi*, Armin Thomas, Callum Birch-Sykes, Michael Wornow, Aman Patel, Stefano Massaroli, Clayton Rabideau, Yoshua Bengio, Stefano Ermon, Stephen Baccus, Chris Re.
 
 **Contact**
 
@@ -112,13 +112,17 @@ Marjan Faizi, Marjan_Faizi@hms.harvard.edu
 ## Citation
 
 
-If you use HyenaDNA in your work, feel free to cite us :)
+Feel free to cite us :)
 
 ```
-
-
-
-
+@article{nguyen2023hyenadna,
+      title={HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution}, 
+      author={Eric Nguyen and Michael Poli and Marjan Faizi and Armin Thomas and Callum Birch-Sykes and Michael Wornow and Aman Patel and Clayton Rabideau and Stefano Massaroli and Yoshua Bengio and Stefano Ermon and Stephen A. Baccus and Chris Ré},
+      year={2023},
+      eprint={2306.15794},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
 
 ```