upd readme.md, renaming to gena-lm

README.md

@@ -4,47 +4,47 @@ tags:
 - human_genome
 ---
 
-# DNALM
+# GENA-LM
 
-DNALM is a transformer masked language model trained on human DNA sequence.
+GENA-LM is a transformer masked language model trained on human DNA sequence.
 
-Differences between DNALM and DNABERT:
+Differences between GENA-LM and DNABERT:
 - BPE tokenization instead of k-mers;
 - input sequence size is about 3000 nucleotides (512 BPE tokens) compared to 510 nucleotides of DNABERT
 - pre-training on T2T vs. GRCh38.p13 human genome assembly.
 
-Source code and data: https://github.com/AIRI-Institute/dna-lm
+Source code and data: https://github.com/AIRI-Institute/GENA-LM
 
 ## Examples
 ### How to load the model to fine-tune it on classification task
 ```python
-from src.dnalm.modeling_bert import BertForSequenceClassification
+from src.gena_lm.modeling_bert import BertForSequenceClassification
 from transformers import AutoTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/dnalm-bert-base')
-model = BertForSequenceClassification.from_pretrained('AIRI-Institute/dnalm-bert-base')
+tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bert-base')
+model = BertForSequenceClassification.from_pretrained('AIRI-Institute/gena-lm-bert-base')
 ```
 
 ## Model description
-DNALM 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 `dnalm-bert-base` is similar to the bert-base:
+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:
 
 - 512 Maximum sequence length
 - 12 Layers, 12 Attention heads
 - 768 Hidden size
 - 32k Vocabulary size
 
-We pre-trained dnalm-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.
+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, dnalm-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.
+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 DNALM on our data and scoring
-After fine-tuning dnalm-bert-base on promoter prediction dataset, following results were achieved: 
+### Fine-tuning GENA-LM on our data and scoring
+After fine-tuning gena-lm-bert-base on promoter prediction dataset, following results were achieved: 
 
-| model                  | seq_len (bp) | F1    |
-|------------------------|--------------|-------|
-| DeePromoter            | 300          | 95.60 |
-| DNALM bert_base (ours) | 2000         | 95.72 |
-| BigBird                | 16000        | 99.90 |
+| model                    | seq_len (bp) | F1    |
+|--------------------------|--------------|-------|
+| DeePromoter              | 300          | 95.60 |
+| GENA-LM bert-base (ours) | 2000         | 95.72 |
+| BigBird                  | 16000        | 99.90 |
 
-We can conclude that our model achieves comparable performance to the previously published results for promoter prediction task.
+We can conclude that our model achieves comparable performance to the previously published results for promoter prediction task.