update readme

README.md

@@ -4,13 +4,15 @@ tags:
 - human_genome
 ---
 
-# GENA-LM
+# GENA-LM (gena-lm-bert-base)
 
-GENA-LM is a transformer masked language model trained on human DNA sequence.
+GENA-LM is a Family of Open-Source Foundational Models for Long DNA Sequences.
 
-Differences between GENA-LM and DNABERT:
+GENA-LM models are transformer masked language models trained on human DNA sequence.
+
+Differences between GENA-LM (`gena-lm-bert-base`) and DNABERT:
 - BPE tokenization instead of k-mers;
-- input sequence size is about 3000 nucleotides (512 BPE tokens) compared to 510 nucleotides of DNABERT
+- input sequence size is about 4500 nucleotides (512 BPE tokens) compared to 512 nucleotides of DNABERT
 - pre-training on T2T vs. GRCh38.p13 human genome assembly.
 
 Source code and data: https://github.com/AIRI-Institute/GENA_LM
@@ -28,32 +30,22 @@ model = BertForSequenceClassification.from_pretrained('AIRI-Institute/gena-lm-be
 ```
 
 ## Model description
-GENA-LM model is trained in a masked language model (MLM) fashion, following the methods proposed in the BigBird paper by masking 85% of tokens. Model config for `gena-lm-bert-base` is similar to the bert-base:
+GENA-LM (`gena-lm-bert-base`) model is trained in a masked language model (MLM) fashion, following the methods proposed in the BigBird paper by masking 15% of tokens. Model config for `gena-lm-bert-base` is similar to the bert-base:
 
 - 512 Maximum sequence length
 - 12 Layers, 12 Attention heads
 - 768 Hidden size
 - 32k Vocabulary size
 
-We pre-trained `gena-lm-bert-base` using the latest T2T human genome assembly (https://www.ncbi.nlm.nih.gov/assembly/GCA_009914755.3/). Pre-training was performed for 500,000 iterations with the same parameters as in BigBird, except sequence length was equal to 512 tokens and we used pre-layer normalization in Transformer.
-
-## Downstream tasks
-Currently, gena-lm-bert-base model has been finetuned  and tested on promoter prediction task.  Its' performance is comparable to previous SOTA results. We plan to fine-tune and make available models for other downstream tasks in the near future.
-
-### Fine-tuning GENA-LM on our data and scoring
-After fine-tuning gena-lm-bert-base on promoter prediction dataset, following results were achieved: 
+We pre-trained `gena-lm-bert-base` using the latest T2T human genome assembly (https://www.ncbi.nlm.nih.gov/assembly/GCA_009914755.3/). Pre-training was performed for 500,000 iterations with the same parameters as in BigBird, except sequence length was equal to 512 tokens. We modified Transformer with [Pre-Layer normalization](https://arxiv.org/abs/2002.04745), but without the final layer LayerNorm.
 
-| model                    | seq_len (bp) | F1    |
-|--------------------------|--------------|-------|
-| DeePromoter              | 300          | 95.60 |
-| GENA-LM bert-base (ours) | 2000         | 95.72 |
-| BigBird                  | 16000        | 99.90 |
+## Evaluation
+For evaluation results, see our paper: https://www.biorxiv.org/content/10.1101/2023.06.12.544594v1
 
-We can conclude that our model achieves comparable performance to the previously published results for promoter prediction task.
 
 ## Citation
-```
-@article {GENA_LM,
+```bibtex
+@article{GENA_LM,
 	author = {Veniamin Fishman and Yuri Kuratov and Maxim Petrov and Aleksei Shmelev and Denis Shepelin and Nikolay Chekanov and Olga Kardymon and Mikhail Burtsev},
 	title = {GENA-LM: A Family of Open-Source Foundational Models for Long DNA Sequences},
 	elocation-id = {2023.06.12.544594},
@@ -64,4 +56,4 @@ We can conclude that our model achieves comparable performance to the previously
 	eprint = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.12.544594.full.pdf},
 	journal = {bioRxiv}
 }
-```
+```