buetnlpbio
/

bidna-bert

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 library_name: transformers
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+<span style="color:red">`buetnlpbio/bidna-bert` was trained on only Human Genome DNA dataset for 1 epoch (for ablation). Performance on other DNA types may be limited. </span>
+# Model Card for Model ID
+BiRNA-BERT is a BERT-style transformer encoder model that generates embeddings for RNA sequences. BiRNA-BERT has been trained on BPE tokens and individual nucleotides. As a result, it can generate both granular nucleotide-level embeddings and efficient sequence-level embeddings (using BPE).
+BiRNA-BERT was trained using the MosaicBERT framework - https://huggingface.co/mosaicml/mosaic-bert-base
+# Usage
+## Extracting RNA embeddings
+```python
+import torch
+import transformers
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("buetnlpbio/bidna-tokenizer")
+config = transformers.BertConfig.from_pretrained("buetnlpbio/bidna-bert")
+mysterybert = AutoModelForMaskedLM.from_pretrained("buetnlpbio/bidna-bert",config=config,trust_remote_code=True)
+mysterybert.cls = torch.nn.Identity()
+# To get sequence embeddings
+seq_embed = mysterybert(**tokenizer("AGCTACGTACGT", return_tensors="pt"))
+print(seq_embed.logits.shape) # CLS + 4 BPE token embeddings + SEP
+# To get nucleotide embeddings
+char_embed = mysterybert(**tokenizer("A G C T A C G T A C G T", return_tensors="pt"))
+print(char_embed.logits.shape) # CLS + 12 nucleotide token embeddings + SEP
+```