Update README.md

README.md

@@ -1,66 +1,67 @@
----
-license: cc-by-nc-sa-4.0
-tags:
-- Helical
-- RNA
-- Biology
-- Transformers
-- Genomics
-- Mamba2
-- Sequence
-library_name: transformers
----
-# Mamba2-mRNA
-Mamba2-mRNA is a state-space model built on the Mamba2 architecture, trained at single-nucleotide resolution. This innovative model offers several advantages, including faster processing speeds compared to traditional transformer models, efficient handling of long sequences, and reduced memory requirements. Its state-space approach enables better modeling of biological sequences by capturing both local and long-range dependencies in mRNA data. The single-nucleotide resolution allows for precise prediction and analysis of genetic elements.
-
-# Helical<a name="helical"></a>
-
-#### Install the package
-
-Run the following to install the [Helical](https://github.com/helicalAI/helical) package via pip:
-```console
-pip install --upgrade helical
-```
-
-#### Generate Embeddings
-```python
-from helical import Mamba2mRNA, Mamba2mRNAConfig
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-input_sequences = ["ACU"*20, "AUG"*20, "AUG"*20, "ACU"*20, "AUU"*20]
-
-mamba2_mrna_config = Mamba2mRNAConfig(batch_size=5, device=device)
-mamba2_mrna = Mamba2mRNA(configurer=mamba2_mrna_config)
-
-# prepare data for input to the model
-processed_input_data = mamba2_mrna.process_data(input_sequences)
-
-# generate the embeddings for the input data
-embeddings = mamba2_mrna.get_embeddings(processed_input_data)
-```
-
-#### Fine-Tuning
-Classification fine-tuning example:
-```python
-from helical import Mamba2mRNAFineTuningModel, Mamba2mRNAConfig
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-input_sequences = ["ACU"*20, "AUG"*20, "AUG"*20, "ACU"*20, "AUU"*20]
-labels = [0, 2, 2, 0, 1]
-
-mamba2_mrna_config = Mamba2mRNAConfig(batch_size=5, device=device, max_length=100)
-mamba2_mrna_fine_tune = Mamba2mRNAFineTuningModel(mamba2_mrna_config=mamba2_mrna_config, fine_tuning_head="classification", output_size=3)
-
-# prepare data for input to the model
-train_dataset = mamba2_mrna_fine_tune.process_data(input_sequences)
-
-# fine-tune the model with the relevant training labels
-mamba2_mrna_fine_tune.train(train_dataset=train_dataset, train_labels=labels)
-
-# get outputs from the fine-tuned model on a processed dataset
-outputs = mamba2_mrna_fine_tune.get_outputs(train_dataset)
-```
+---
+license: cc-by-nc-sa-4.0
+tags:
+- Helical
+- rna
+- mrna
+- biology
+- transformers
+- mamba2
+- sequence
+- genomics
+library_name: transformers
+---
+# Mamba2-mRNA
+Mamba2-mRNA is a state-space model built on the Mamba2 architecture, trained at single-nucleotide resolution. This innovative model offers several advantages, including faster processing speeds compared to traditional transformer models, efficient handling of long sequences, and reduced memory requirements. Its state-space approach enables better modeling of biological sequences by capturing both local and long-range dependencies in mRNA data. The single-nucleotide resolution allows for precise prediction and analysis of genetic elements.
+
+# Helical<a name="helical"></a>
+
+#### Install the package
+
+Run the following to install the [Helical](https://github.com/helicalAI/helical) package via pip:
+```console
+pip install --upgrade helical
+```
+
+#### Generate Embeddings
+```python
+from helical import Mamba2mRNA, Mamba2mRNAConfig
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+input_sequences = ["ACU"*20, "AUG"*20, "AUG"*20, "ACU"*20, "AUU"*20]
+
+mamba2_mrna_config = Mamba2mRNAConfig(batch_size=5, device=device)
+mamba2_mrna = Mamba2mRNA(configurer=mamba2_mrna_config)
+
+# prepare data for input to the model
+processed_input_data = mamba2_mrna.process_data(input_sequences)
+
+# generate the embeddings for the input data
+embeddings = mamba2_mrna.get_embeddings(processed_input_data)
+```
+
+#### Fine-Tuning
+Classification fine-tuning example:
+```python
+from helical import Mamba2mRNAFineTuningModel, Mamba2mRNAConfig
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+input_sequences = ["ACU"*20, "AUG"*20, "AUG"*20, "ACU"*20, "AUU"*20]
+labels = [0, 2, 2, 0, 1]
+
+mamba2_mrna_config = Mamba2mRNAConfig(batch_size=5, device=device, max_length=100)
+mamba2_mrna_fine_tune = Mamba2mRNAFineTuningModel(mamba2_mrna_config=mamba2_mrna_config, fine_tuning_head="classification", output_size=3)
+
+# prepare data for input to the model
+train_dataset = mamba2_mrna_fine_tune.process_data(input_sequences)
+
+# fine-tune the model with the relevant training labels
+mamba2_mrna_fine_tune.train(train_dataset=train_dataset, train_labels=labels)
+
+# get outputs from the fine-tuned model on a processed dataset
+outputs = mamba2_mrna_fine_tune.get_outputs(train_dataset)
+```