---
library_name: transformers
tags: []
---

# ChatNT

[ChatNT](https://www.biorxiv.org/content/10.1101/2024.04.30.591835v1) is the first multimodal conversational agent designed with a deep understanding of biological sequences (DNA, RNA, proteins). 
It enables users — even those with no coding background — to interact with biological data through natural language. neralizes well across multiple biological tasks and modalities.


**Developed by:** [InstaDeep](https://huggingface.co/InstaDeepAI)

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** [Nucleotide Transformer](https://github.com/instadeepai/nucleotide-transformer)
- **Paper:** [ChatNT: A Multimodal Conversational Agent for DNA, RNA and Protein Tasks](https://www.biorxiv.org/content/10.1101/2024.04.30.591835v1.full.pdf) 


### How to use

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

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

A small snippet of code is given here in order to **generate sequences from a pipeline (high-level)**.

```
# Load pipeline
from transformers import pipeline
pipe = pipeline(model="InstaDeepAI/ChatNT-text-generation-pipeline", trust_remote_code=True)

# Define custom inputs (note that the number of <DNA> token in the english sequence must be equal to len(dna_sequences))
english_sequence = "Is there any evidence of an acceptor splice site in this sequence <DNA> ?"
dna_sequences = ["ATCGGAAAAAGATCCAGAAAGTTATACCAGGCCAATGGGAATCACCTATTACGTGGATAATAGCGATAGTATGTTACCTATAAATTTAACTACGTGGATATCAGGCAGTTACGTTACCAGTCAAGGAGCACCCAAAACTGTCCAGCAACAAGTTAATTTACCCATGAAGATGTACTGCAAGCCTTGCCAACCAGTTAAAGTAGCTACTCATAAGGTAATAAACAGTAATATCGACTTTTTATCCATTTTGATAATTGATTTATAACAGTCTATAACTGATCGCTCTACATAATCTCTATCAGATTACTATTGACACAAACAGAAACCCCGTTAATTTGTATGATATATTTCCCGGTAAGCTTCGATTTTTAATCCTATCGTGACAATTTGGAATGTAACTTATTTCGTATAGGATAAACTAATTTACACGTTTGAATTCCTAGAATATGGAGAATCTAAAGGTCCTGGCAATGCCATCGGCTTTCAATATTATAATGGACCAAAAGTTACTCTATTAGCTTCCAAAACTTCGCGTGAGTACATTAGAACAGAAGAATAACCTTCAATATCGAGAGAGTTACTATCACTAACTATCCTATG"]

# Generate sequence
generated_english_sequence = pipe(
  inputs={
    "english_sequence": english_sequence,
    "dna_sequences": dna_sequences
  }
)
```

A small snippet of code is given here in order to **infer with the model without any abstraction (low-level)**.

```
import numpy as np
from transformers import AutoModel, AutoTokenizer

# Load model and tokenizers
model = AutoModel.from_pretrained("InstaDeepAI/ChatNT", trust_remote_code=True)
english_tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/ChatNT", subfolder="english_tokenizer")
bio_tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/ChatNT", subfolder="bio_tokenizer")

# Define custom inputs (note that the number of <DNA> token in the english sequence must be equal to len(dna_sequences))
english_sequence = "A chat between a curious user and an artificial intelligence assistant that can handle bio sequences. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Is there any evidence of an acceptor splice site in this sequence <DNA> ?"
dna_sequences = ["ATCGGAAAAAGATCCAGAAAGTTATACCAGGCCAATGGGAATCACCTATTACGTGGATAATAGCGATAGTATGTTACCTATAAATTTAACTACGTGGATATCAGGCAGTTACGTTACCAGTCAAGGAGCACCCAAAACTGTCCAGCAACAAGTTAATTTACCCATGAAGATGTACTGCAAGCCTTGCCAACCAGTTAAAGTAGCTACTCATAAGGTAATAAACAGTAATATCGACTTTTTATCCATTTTGATAATTGATTTATAACAGTCTATAACTGATCGCTCTACATAATCTCTATCAGATTACTATTGACACAAACAGAAACCCCGTTAATTTGTATGATATATTTCCCGGTAAGCTTCGATTTTTAATCCTATCGTGACAATTTGGAATGTAACTTATTTCGTATAGGATAAACTAATTTACACGTTTGAATTCCTAGAATATGGAGAATCTAAAGGTCCTGGCAATGCCATCGGCTTTCAATATTATAATGGACCAAAAGTTACTCTATTAGCTTCCAAAACTTCGCGTGAGTACATTAGAACAGAAGAATAACCTTCAATATCGAGAGAGTTACTATCACTAACTATCCTATG"]

# Tokenize
english_tokens = english_tokenizer(english_sequence, return_tensors="pt", padding="max_length", truncation=True, max_length=512).input_ids
bio_tokens = bio_tokenizer(dna_sequences, return_tensors="pt", padding="max_length", max_length=512, truncation=True).input_ids.unsqueeze(0) # unsqueeze to simulate batch_size = 1

# Predict
outs = model(
    multi_omics_tokens_ids=(english_tokens, bio_tokens),
    projection_english_tokens_ids=english_tokens,
    projected_bio_embeddings=None,
)
```