Improve model card with metadata and links

README.md

@@ -1,34 +1,34 @@
 ---
-tags:
-- generated_from_trainer
 metrics:
 - accuracy
+tags:
+- generated_from_trainer
+license: mit
+library_name: transformers
+pipeline_tag: feature-extraction
 model-index:
 - name: base_5120
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # base_5120
 
-This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 5.0263
-- Accuracy: 0.2365
+This model is a BERT-based DNA language model trained with the standard byte-pair encoding (BPE) tokenizer with vocabulary size 5,120. It is the baseline control model from the paper [EvoLen: Evolution-Guided Tokenization for DNA Language Model](https://huggingface.co/papers/2604.08698) (COLM 2026).
+
+- **Paper**: [EvoLen: Evolution-Guided Tokenization for DNA Language Model](https://huggingface.co/papers/2604.08698)
+- **Code**: [https://github.com/HN020719/EvoLen](https://github.com/HN020719/EvoLen)
 
 ## Model description
 
-More information needed
+This model uses a BERT-base architecture (12 layers, hidden size 768) with a vocabulary of 5,120 tokens. It was pretrained on human genome sequences using masked language modeling. The tokenizer is the standard BPE baseline described in the EvoLen repository.
 
 ## Intended uses & limitations
 
-More information needed
+This model is primarily intended as a baseline for evaluating the EvoLen tokenizer in DNA language model benchmarks. It can be fine-tuned for downstream genomic tasks, but it is not specifically designed for production use.
 
 ## Training and evaluation data
 
-More information needed
+The model was pretrained on the human genome (hg38). Training sequences were tokenized into 512-token windows and a held-out evaluation set was used to report the following results.
 
 ## Training procedure
 
@@ -46,11 +46,13 @@ The following hyperparameters were used during training:
 
 ### Training results
 
+- Loss: 5.0263
+- Accuracy: 0.2365
+- Perplexity: 152.36
 
-
-### Framework versions
+## Framework versions
 
 - Transformers 4.35.2
 - Pytorch 2.8.0+cu128
 - Datasets 4.4.1
-- Tokenizers 0.15.2
+- Tokenizers 0.15.2