Nucleotide Transformer 500m 1000g

🚀 nucleotide-transformer-500m-1000g model

The Nucleotide Transformers are foundational language models pre - trained on whole - genome DNA sequences. They leverage diverse genomes for more accurate molecular phenotype prediction compared to existing methods.

🚀 Quick Start

Until its next release, the transformers library needs to be installed from source with the following command in order to use the models:

pip install --upgrade git+https://github.com/huggingface/transformers.git

✨ Features

• The Nucleotide Transformers integrate information from over 3,200 diverse human genomes and 850 genomes from a wide range of species.
• The nucleotide-transformer-500m-1000g is a 500M parameters transformer pre - trained on 3202 genetically diverse human genomes.
• The model is available in both Tensorflow and Pytorch.

📦 Installation

To use the models, you need to install the transformers library from source:

pip install --upgrade git+https://github.com/huggingface/transformers.git

💻 Usage Examples

Basic Usage

A small snippet of code is given here in order to retrieve both logits and embeddings from a dummy DNA sequence.

from transformers import AutoTokenizer, AutoModelForMaskedLM
import torch

# Import the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/nucleotide-transformer-500m-1000g")
model = AutoModelForMaskedLM.from_pretrained("InstaDeepAI/nucleotide-transformer-500m-1000g")

# Choose the length to which the input sequences are padded. By default, the 
# model max length is chosen, but feel free to decrease it as the time taken to 
# obtain the embeddings increases significantly with it.
max_length = tokenizer.model_max_length

# Create a dummy dna sequence and tokenize it
sequences = ["ATTCCGATTCCGATTCCG", "ATTTCTCTCTCTCTCTGAGATCGATCGATCGAT"]
tokens_ids = tokenizer.batch_encode_plus(sequences, return_tensors="pt", padding="max_length", max_length = max_length)["input_ids"]

# Compute the embeddings
attention_mask = tokens_ids != tokenizer.pad_token_id
torch_outs = model(
    tokens_ids,
    attention_mask=attention_mask,
    encoder_attention_mask=attention_mask,
    output_hidden_states=True
)

# Compute sequences embeddings
embeddings = torch_outs['hidden_states'][-1].detach().numpy()
print(f"Embeddings shape: {embeddings.shape}")
print(f"Embeddings per token: {embeddings}")

# Add embed dimension axis
attention_mask = torch.unsqueeze(attention_mask, dim=-1)

# Compute mean embeddings per sequence
mean_sequence_embeddings = torch.sum(attention_mask*embeddings, axis=-2)/torch.sum(attention_mask, axis=1)
print(f"Mean sequence embeddings: {mean_sequence_embeddings}")

📚 Documentation

Model Sources

• Repository: Nucleotide Transformer
• Paper: The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics

Training data

The nucleotide-transformer-500m-1000g model was pretrained on 3202 genetically diverse human genomes originating from 27 geographically structured populations of African, American, East Asian, and European ancestry taken from the 1000G project . Such diversity allowed the dataset to encode a better representation of human genetic variation. To allow haplotype reconstruction in the sequences fed to the model, we considered the phased version of the 1000G Genomes project, which corresponded to a total of 125M mutations, 111M and 14M of which are single nucleotide polymorphisms (SNPs) and indels, respectively. The total number of nucleotides in the dataset is 19,212 B nucleotides, resulting in roughly 3,202 B tokens.

Training procedure

Preprocessing

The DNA sequences are tokenized using the Nucleotide Transformer Tokenizer, which tokenizes sequences as 6 - mers tokenizer when possible, otherwise tokenizing each nucleotide separately as described in the Tokenization section of the associated repository. This tokenizer has a vocabulary size of 4105. The inputs of the model are then of the form:

<CLS> <ACGTGT> <ACGTGC> <ACGGAC> <GACTAG> <TCAGCA>

The tokenized sequence have a maximum length of 1,000.

The masking procedure used is the standard one for Bert - style training:

• 15% of the tokens are masked.
• In 80% of the cases, the masked tokens are replaced by [MASK].
• In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
• In the 10% remaining cases, the masked tokens are left as is.

Pretraining

The model was trained with 8 A100 80GB on 300B tokens, with an effective batch size of 1M tokens. The sequence length used was 1000 tokens. The Adam optimizer [38] was used with a learning rate schedule, and standard values for exponential decay rates and epsilon constants, β1 = 0.9, β2 = 0.999 and ε = 1e - 8. During a first warmup period, the learning rate was increased linearly between 5e - 5 and 1e - 4 over 16k steps before decreasing following a square root decay until the end of training.

BibTeX entry and citation info

@article{dalla2023nucleotide,
  title={The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics},
  author={Dalla - Torre, Hugo and Gonzalez, Liam and Mendoza Revilla, Javier and Lopez Carranza, Nicolas and Henryk Grywaczewski, Adam and Oteri, Francesco and Dallago, Christian and Trop, Evan and Sirelkhatim, Hassan and Richard, Guillaume and others},
  journal={bioRxiv},
  pages={2023--01},
  year={2023},
  publisher={Cold Spring Harbor Laboratory}
}

📄 License

This model is released under the cc-by-nc-sa-4.0 license.

Additional Information