update readme

README.md

@@ -4,13 +4,17 @@ tags:
 - human_genome
 ---
 
-# GENA-LM (BigBird-base T2T)
+# GENA-LM (gena-lm-bigbird-base-t2t)
 
-GENA-LM (BigBird-base T2T) is a transformer masked language model trained on human DNA sequence. GENA-LM (BigBird-base T2T) follows BigBird architecture.
+GENA-LM is a Family of Open-Source Foundational Models for Long DNA Sequences.
 
-Differences between GENA-LM (BigBird-base T2T) and DNABERT:
+GENA-LM models are transformer masked language models trained on human DNA sequence. 
+
+`gena-lm-bigbird-base-t2t` follows the BigBird architecture and its HuggingFace implementation.
+
+Differences between GENA-LM (`gena-lm-bigbird-base-t2t`) and DNABERT:
 - BPE tokenization instead of k-mers;
-- input sequence size is about 24000 nucleotides (4096 BPE tokens) compared to 510 nucleotides of DNABERT;
+- input sequence size is about 36000 nucleotides (4096 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
@@ -37,7 +41,7 @@ model = BigBirdForSequenceClassification.from_pretrained('AIRI-Institute/gena-lm
 ```
 
 ## Model description
-GENA-LM (BigBird-base T2T) 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-bigbird-base-t2t` is similar to the `google/bigbird-roberta-base`:
+GENA-LM (`gena-lm-bigbird-base-t2t`) 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-bigbird-base-t2t` is similar to the `google/bigbird-roberta-base`:
 
 - 4096 Maximum sequence length
 - 12 Layers, 12 Attention heads
@@ -49,11 +53,14 @@ GENA-LM (BigBird-base T2T) model is trained in a masked language model (MLM) fas
     - sliding window blocks: 3
 - 32k Vocabulary size, tokenizer trained on DNA data.
 
-We pre-trained `gena-lm-bigbird-base-t2t` using the latest T2T human genome assembly (https://www.ncbi.nlm.nih.gov/assembly/GCA_009914755.3/). The data was augmented by sampling SNPs human mutations.  Pre-training was performed for 1,070,000 iterations with batch size 256.
+We pre-trained `gena-lm-bigbird-base-t2t` using the latest T2T human genome assembly (https://www.ncbi.nlm.nih.gov/assembly/GCA_009914755.3/). The data was augmented by sampling mutations from 1000-genome SNPs (gnomAD dataset). Pre-training was performed for 1,070,000 iterations with batch size 256.
+
+## Evaluation
+For evaluation results, see our paper: https://www.biorxiv.org/content/10.1101/2023.06.12.544594v1
 
 ## 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 +71,4 @@ We pre-trained `gena-lm-bigbird-base-t2t` using the latest T2T human genome asse
 	eprint = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.12.544594.full.pdf},
 	journal = {bioRxiv}
 }
-```
+```