Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+tags:
+- RoBERTa
+- RNA
+- LLM
+- RNA sequence
+language:
+- en
 ---
 
 # 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. -->
+<!-- Provide a quick summary of what the model is/does. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+**RNA-BERTa** is a lightweight BERT model trained following the RoBERTa approach.  
+It features a context window of 512 tokens and an embedding dimension of 512 (compared to the standard 768), resulting in approximately 55.56 million parameters. This design aligns with the 1:20 parameter-to-token compute-optimal ratio suggested by [Hoffmann et al.](https://doi.org/10.48550/arXiv.2203.15556).
+The model was pretrained on a masked language modeling (MLM) task using 9,757,119 RNA sequences sourced from [RNACentral](https://doi.org/10.1093/nar/gkaa921) and [NCBI](https://doi.org/10.1093/nar/gkae979), totaling 1.07 billion training tokens.
 
-## Uses
+RNA-BERTa can be fine-tuned or utilized to generate embeddings from RNA sequences for various downstream applications and other related tasks.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+- **Developed by:** Pasquale Lobascio
+- **Shared by:** IlPakoZ
+- **Model type:** RoBERTa-based Transformer with MLM head
+- **License:** Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+RNA-BERTa can be used to generate embeddings from RNA sequences, which can be applied to various downstream biological sequence analysis tasks.
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model can be fine-tuned for a wide range of RNA-related tasks, such as classification, motif detection, or other predictive modeling involving RNA sequences.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is **not** intended for RNA sequences longer than 512 tokens, as the context length is limited. It may not perform well on tasks unrelated to RNA sequence embeddings or Masked Language Modeling.
 
 ## 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.
+- The model’s context length is limited to 512 tokens, which restricts its use on longer RNA sequences.
+- As the model was pretrained on specific datasets, it may have biases related to sequence representation or coverage.
+- Potential biases or limitations inherent in the training data (RNACentral and NCBI) may affect downstream tasks.
+- This is a domain-specific model; it may not generalize outside RNA sequence analysis.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```python
+from transformers import RobertaForMaskedLM, RobertaTokenizerFast, RobertaModel
 
-[More Information Needed]
+# Load with MLM head
+model = RobertaForMaskedLM.from_pretrained("IlPakoZ/RNA-BERTa9700")
+tokenizer = RobertaTokenizerFast.from_pretrained("IlPakoZ/RNA-BERTa9700")
 
-## Training Details
+# Alternatively, load only the encoder for downstream tasks
+encoder = RobertaModel.from_pretrained("IlPakoZ/RNA-BERTa9700")
+tokenizer = RobertaTokenizerFast.from_pretrained("IlPakoZ/RNA-BERTa9700")
+```
 
 ### 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]
+The pretraining dataset includes a total of 10,841,246 RNA sequences. The data was divided into training set (9,757,119 sequences) and validation set (1,084,127 sequences),  collected from [RNACentral](https://doi.org/10.1093/nar/gkaa921) and [NCBI](https://doi.org/10.1093/nar/gkae979), for a total of ~1.22 B tokens (of which ~1.07 B training tokens).<br>The data includes the following RNA types:
+- 7,979,027 ribosomal RNA sequences, collected from SILVA database;
+- 492,955 (pre and mature) miRNA sequences, collected from RNACentral databases;
+- 3,137 repeats sequences, collected from RNACentral databases;
+- 90,467 riboswitches sequences, collected from RNACentral databases;
+- 29,581 ribozymes sequences, collected from RNACentral databases;
+- 2,246,079 virus sequences, collected from NCBI virus database.
+
+Only sequences up to 2,000 nucleotides long were selected.
 
 ### 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]
-
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-#### 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 -->
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-#### 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. -->
+The pretraining of RNA-BERTa consisted of three main steps:
 
-[More Information Needed]
+1. **Hyperparameter Optimization (HO):**  
+   We performed 32 trials of HO using Optuna with a Tree-structured Parzen Estimator (TPE) sampler. Due to the high cost of tuning large transformer models, we leveraged **μParametrization (μP)** [Yang et al., 2022](https://doi.org/10.48550/arXiv.2203.03466) to optimize learning rate and warm-up steps on a smaller version of RNA-BERTa. This allowed us to generalize the hyperparameters to the full model efficiently. Because μP only transfers non-regularization parameters, weight decay was fixed at 0.01.  
+   To avoid instability reported with 16-bit floating point values during HO with μP [Blake et al., 2024](https://doi.org/10.48550/arXiv.2407.17465), we used full precision. Training during HO was limited to 5,000 steps per model (instead of the full 38,000 steps), which saved considerable compute while maintaining precise hyperparameter transfer for this post-layer normalization transformer architecture.  
+   To cover slight variations in the optimal learning rate, three final models were trained using learning rates scaled by factors of 1, 0.875, and 0.75 from the HO optimum. Overall, this approach improved efficiency by approximately 4.5× compared to full-scale HO. HO was conducted on 4 NVIDIA V100 GPUs with 150 GB RAM and completed in about two days.
 
-#### Metrics
+2. **Pretraining Schedule:**  
+   Throughout pretraining, we used cosine learning rate scheduling with warm-up and an approximate 10× decay over one full training epoch, following recommendations from [Rae et al.](https://doi.org/10.48550/arXiv.2112.11446) and the Chinchilla scaling laws [Hoffmann et al., 2022](https://doi.org/10.48550/arXiv.2203.15556).
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+3. **Implementation Details:**  
+   The μP implementations of AdamW and RoBERTa from the original μP authors were employed. An attention multiplier of √32 was used to enable smooth parameter transfer during subsequent fine-tuning.
 
-[More Information Needed]
+The overall pretraining workflow is illustrated in Figure \ref{fig:ho}.
 
-### Results
-
-[More Information Needed]
-
-#### Summary
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-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
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-## 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]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
-
-[More Information Needed]
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-## Citation [optional]
+## Citation
 
 <!-- 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]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
-
-[More Information Needed]
+```bibtext
+@article{10.48550/arXiv.2203.15556,
+  title={Training compute-optimal large language models},
+  author={Hoffmann, Jordan and Borgeaud, Sebastian and Mensch, Arthur and Buchatskaya, Elena and Cai, Trevor and Rutherford, Eliza and Casas, Diego de Las and Hendricks, Lisa Anne and Welbl, Johannes and Clark, Aidan and others},
+  journal={arXiv preprint arXiv:2203.15556},
+  year={2022}
+},
+@article{10.1093/nar/gkaa921,
+  title={RNAcentral 2021: secondary structure integration, improved sequence search and new member databases},
+  journal={Nucleic acids research},
+  volume={49},
+  number={D1},
+  pages={D212--D220},
+  year={2021},
+  publisher={Oxford University Press}
+},
+@article{10.1093/nar/gkae979,
+  title={Database resources of the National Center for Biotechnology Information in 2025},
+  author={Sayers, Eric W and Beck, Jeffrey and Bolton, Evan E and Brister, J Rodney and Chan, Jessica and Connor, Ryan and Feldgarden, Michael and Fine, Anna M and Funk, Kathryn and Hoffman, Jinna and others},
+  journal={Nucleic acids research},
+  volume={53},
+  number={D1},
+  pages={D20--D29},
+  year={2025},
+  publisher={Oxford University Press}
+},
+@article{10.48550/arXiv.2203.03466,
+  title={Tensor programs v: Tuning large neural networks via zero-shot hyperparameter transfer},
+  author={Yang, Greg and Hu, Edward J and Babuschkin, Igor and Sidor, Szymon and Liu, Xiaodong and Farhi, David and Ryder, Nick and Pachocki, Jakub and Chen, Weizhu and Gao, Jianfeng},
+  journal={arXiv preprint arXiv:2203.03466},
+  year={2022}
+},
+
+@article{10.48550/arXiv.2112.11446,
+  title={Scaling language models: Methods, analysis \& insights from training gopher},
+  author={Rae, Jack W and Borgeaud, Sebastian and Cai, Trevor and Millican, Katie and Hoffmann, Jordan and Song, Francis and Aslanides, John and Henderson, Sarah and Ring, Roman and Young, Susannah and others},
+  journal={arXiv preprint arXiv:2112.11446},
+  year={2021}
+},
+
+@article{10.48550/arXiv.2407.17465,
+  title={u-$\mu$P: The Unit-Scaled Maximal Update Parametrization},
+  author={Blake, Charlie and Eichenberg, Constantin and Dean, Josef and Balles, Lukas and Prince, Luke Y and Deiseroth, Bj{\"o}rn and Cruz-Salinas, Andres Felipe and Luschi, Carlo and Weinbach, Samuel and Orr, Douglas},
+  journal={arXiv preprint arXiv:2407.17465},
+  year={2024}
+}
+
+```