omicseye
/

seqLens_8192_512_30M

@@ -1,199 +1,62 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
 <!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 ## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
 library_name: transformers
+tags:
+- DNA
+- genomics
+datasets:
+- omicseye/prok_heavy
 ---
+## Introduction
+The seqLens models are a collection of genomic language models.
+seqLens models leverage an extensive dataset of 19,551 reference genomes,
+including over 18,000 prokaryotic genomes (115B nucleotides),
+alongside a more balanced dataset of 1,354 genomes spanning 1,166 prokaryotic and 188 eukaryotic reference genomes (180B nucleotides).
+Through systematic evaluation of 52 DNA language models with varying architectures, hyperparameters, and classification heads,
+we developed seqLens, a family of models based on disentangled attention with relative positional encoding.
+These models demonstrate superior performance, outperforming state-of-the-art methods in phenotypic predictions.
+The seqLens models provide a robust foundation for optimizing DNA language models and advancing genome annotations across diverse biological contexts.
+- **Developed by:** omicseye
+- **Model type:** Encoder
+- **Language(s) (NLP):** DNA
+- **pretraining dataset:** omicseye/prok_heavy
+- **License:** The model is made available under the [CC-BY-NC 4.0 License]. For inquiries about commercial licensing, please contact [email protected].
+<p align="center">
+  <img width="100%" src="https://github.com/omicsEye/seqLens/blob/main/visualizations/plots/png/model_performance_all.png?raw=true">
+</p>
+### Model Sources
 <!-- Provide the basic links for the model. -->
+- **Repository:** https://github.com/omicsEye/seqLens
+- **Paper:** https://doi.org/10.1101/2025.03.12.642848
 ## How to Get Started with the Model
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+tokenizer = AutoTokenizer.from_pretrained("omicseye/seqLens_8192_512_30M")
+model = AutoModelForMaskedLM.from_pretrained("omicseye/seqLens_8192_512_30M")
+```
+## Citation
+```bibtex
+@article {seqLens,
+	author = {Baghbanzadeh, Mahdi and Mann, Brendan and Crandall, Keith A and Rahnavard, Ali},
+	title = {seqLens: optimizing language models for genomic predictions},
+	elocation-id = {2025.03.12.642848},
+	year = {2025},
+	doi = {10.1101/2025.03.12.642848},
+	publisher = {Cold Spring Harbor Laboratory},
+	URL = {https://www.biorxiv.org/content/early/2025/03/14/2025.03.12.642848},
+	eprint = {https://www.biorxiv.org/content/early/2025/03/14/2025.03.12.642848.full.pdf},
+	journal = {bioRxiv}
+}
+```