MsAlEhR
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MetaBERTa-bigbird-gene

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-library_name: transformers
-tags: []
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-# Model Card for Model ID
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-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+### Model Card for "Leveraging Large Language Models for Metagenomic Analysis"
+**Model Overview:**
+The model presented in this paper is based on the BigBird transformer architecture and is trained specifically on gene sequences. This model aims to uncover insights within metagenomic data and is evaluated on various tasks such as classification and sequence embedding.
+**Model Architecture:**
+- **Base Model:** BigBird transformer architecture
+- **Tokenizer:** Custom K-mer Tokenizer with k-mer length of 6 and overlapping tokens
+- **Training:** Trained on a diverse dataset of gene sequences
+- **Embeddings:** Generates sequence embeddings using both mean and max pooling of hidden states
+**Dataset:**
+Details of the dataset will be shared in the supplementary materials of the paper. The dataset includes a comprehensive collection of gene sequences from various metagenomic sources.
+**Usage:**
+To use the model, you need to download the KmerTokenizer from the specified repository and import it before using the model.
+**Steps to Use the Model:**
+1. **Install KmerTokenizer:**
+   Download KmerTokenizer separately from the following repository:
+   [KmerTokenizer Repository](https://huggingface.co/MsAlEhR/MetaBERTa-bigbird-gene/tree/main)
+2. **Example Code:**
+   ```python
+   from KmerTokenizer import KmerTokenizer
+   from transformers import AutoModel
+   import torch
+   # Example gene sequence
+   seq_list = ["ATTTTTTTTTTTCCCCCCCCCCCGGGGGGGGATCGATGC"]
+   # Initialize the tokenizer
+   tokenizer = KmerTokenizer(kmerlen=6, overlapping=True, maxlen=4096)
+   tokenized_output = tokenizer.kmer_tokenize(seq_list)
+   # Convert tokenized output to tensor
+   inputs = torch.tensor(tokenized_output)
+   # Load the pre-trained BigBird model
+   model = AutoModel.from_pretrained("MsAlEhR/MetaBERTa-bigbird-gene", output_hidden_states=True)
+   # Generate hidden states
+   hidden_states = model(inputs)[0]
+   # Compute mean and max pooling of the hidden states
+   embedding_mean = torch.mean(hidden_states[-1], dim=1)
+   embedding_max = torch.max(hidden_states[-1], dim=1)
+   ```
+**Citation:**
+For a detailed overview of leveraging large language models for metagenomic analysis, refer to our paper:
+> Refahi, M.S., Sokhansanj, B.A., & Rosen, G.L. (Year). Leveraging Large Language Models for Metagenomic Analysis. *IEEE*.