Add pipeline tag, library name and license

README.md

@@ -5,16 +5,20 @@ metrics:
 tags:
 - biology
 - medical
+pipeline_tag: feature-extraction
+library_name: transformers
+license: mit
 ---
+
 This is the official pre-trained baseline model introduced in [Fast and Low-Cost Genomic Foundation Models via Outlier Removal
-](https://arxiv.org/abs/2505.00598).
+](https://huggingface.co/papers/2505.00598).
 
 We sincerely appreciate the MosaicML team for the [MosaicBERT](https://openreview.net/forum?id=5zipcfLC2Z) implementation, which serves as the base of DNABERT-2 development. 
 
 DNABERT-2 is a transformer-based genome foundation model trained on multi-species genome. 
 
 To load the model from huggingface:
-```
+```python
 import torch
 from transformers import AutoTokenizer, AutoModel
 
@@ -23,7 +27,7 @@ model = AutoModel.from_pretrained("zhihan1996/DNABERT-2-117M", trust_remote_code
 ```
 
 To calculate the embedding of a dna sequence
-```
+```python
 dna = "ACGTAGCATCGGATCTATCTATCGACACTTGGTTATCGATCTACGAGCATCTCGTTAGC"
 inputs = tokenizer(dna, return_tensors = 'pt')["input_ids"]
 hidden_states = model(inputs)[0] # [1, sequence_length, 768]