InstaDeepAI
/

segment_nt

@@ -8,9 +8,9 @@ tags:
 - genomics
 - segmentation
 ---
-# segment-nt-30kb
-Segment-NT-30kb is a segmentation model leveraging the [Nucleotide Transformer](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-500m-multi-species) (NT) DNA foundation model to predict the location of several types of genomics
 elements in a sequence at a single nucleotide resolution. It was trained on 14 different classes of human genomics elements in input sequences up to 30kb. These
 include gene (protein-coding genes, lncRNAs, 5’UTR, 3’UTR, exon, intron, splice acceptor and donor sites) and regulatory (polyA signal, tissue-invariant and
 tissue-specific promoters and enhancers, and CTCF-bound sites) elements.
@@ -63,8 +63,8 @@ features = [
     "promoter_Tissue_invariant",
   ]
-tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/segment_nt_30kb", trust_remote_code=True)
-model = AutoModel.from_pretrained("InstaDeepAI/segment_nt_30kb", trust_remote_code=True)
 # Choose the length to which the input sequences are padded. By default, the
 # model max length is chosen, but feel free to decrease it as the time taken to
@@ -106,7 +106,7 @@ print(f"Intron probabilities shape: {probabilities_intron.shape}")
 ## Training data
-The **segment-nt-30kb** model was trained on all human chromosomes except for chromosomes 20 and 21, kept as test set, and chromosome 22, used as a validation set.
 During training, sequences are randomly sampled in the genome with associated annotations. However, we keep the sequences in the validation and test set fixed by
 using a sliding window of length 30,000 over the chromosomes 20 and 21. The validation set was used to monitor training and for early stopping.

 - genomics
 - segmentation
 ---
+# segment-nt
+Segment-NT is a segmentation model leveraging the [Nucleotide Transformer](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-500m-multi-species) (NT) DNA foundation model to predict the location of several types of genomics
 elements in a sequence at a single nucleotide resolution. It was trained on 14 different classes of human genomics elements in input sequences up to 30kb. These
 include gene (protein-coding genes, lncRNAs, 5’UTR, 3’UTR, exon, intron, splice acceptor and donor sites) and regulatory (polyA signal, tissue-invariant and
 tissue-specific promoters and enhancers, and CTCF-bound sites) elements.
     "promoter_Tissue_invariant",
   ]
+tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/segment_nt", trust_remote_code=True)
+model = AutoModel.from_pretrained("InstaDeepAI/segment_nt", trust_remote_code=True)
 # Choose the length to which the input sequences are padded. By default, the
 # model max length is chosen, but feel free to decrease it as the time taken to
 ## Training data
+The **segment-nt** model was trained on all human chromosomes except for chromosomes 20 and 21, kept as test set, and chromosome 22, used as a validation set.
 During training, sequences are randomly sampled in the genome with associated annotations. However, we keep the sequences in the validation and test set fixed by
 using a sliding window of length 30,000 over the chromosomes 20 and 21. The validation set was used to monitor training and for early stopping.