Improve model card with metadata and links

README.md

@@ -1,34 +1,35 @@
 ---
-tags:
-- generated_from_trainer
+license: mit
+library_name: transformers
+pipeline_tag: feature-extraction
 metrics:
 - accuracy
+tags:
+- generated_from_trainer
 model-index:
 - name: model_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. -->
-
 # model_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: 5.0825
-- Accuracy: 0.2308
+This model is a BERT-based masked language model with a 5,120-token vocabulary, trained on human genomic sequences as part of the [EvoLen](https://github.com/HN020719/EvoLen) study: [EvoLen: Evolution-Guided Tokenization for DNA Language Model](https://huggingface.co/papers/2604.08698).
 
 ## Model description
 
-More information needed
+The model uses a BERT-base architecture (12 layers, 768 hidden units, 12 attention heads) and was trained with a masked language modeling objective on DNA sequences tokenized into 512-token windows. It can be used as a foundation model for downstream DNA sequence tasks or for feature extraction.
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended for research on DNA language modeling, including fine-tuning on genomic benchmarks such as regulatory element classification and ATAC-seq tasks. It was trained on human reference genome (hg38) data and may not generalize to other organisms or non-conserved sequences without further adaptation.
 
 ## Training and evaluation data
 
-More information needed
+The model was pretrained on the human genome (hg38) using masked language modeling. Evaluation was performed on a held-out split of the same corpus. The evaluation results are:
+
+- Loss: 5.0825
+- Accuracy: 0.2308
+- Perplexity: 161.17
 
 ## Training procedure
 
@@ -50,7 +51,10 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-
+- Train loss: 5.6056
+- Eval loss: 5.0825
+- Eval accuracy: 0.2308
+- Perplexity: 161.17
 
 ### Framework versions
 
@@ -58,3 +62,18 @@ The following hyperparameters were used during training:
 - Pytorch 2.8.0+cu128
 - Datasets 4.4.1
 - Tokenizers 0.15.2
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@inproceedings{huang2026evolen,
+  title     = {EvoLen: Evolution-Guided Tokenization for DNA Language Model},
+  author    = {Huang, Nan and Zhou, Xiaoxiao and Cui, Junxia and
+               Tapia-Pacheco, Mario and Amariuta, Tiffany and Li, Yang and
+               Shang, Jingbo},
+  booktitle = {Conference on Language Modeling (COLM)},
+  year      = {2026}
+}
+```