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library_name: transformers
tags: []
---
### 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 infer with the model from a given input.
```
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_tokenized_sequence_length = 512
bio_tokenized_sequence_length = 512
english_tokens = english_tokenizer(english_sequence, return_tensors="pt", padding="max_length", truncation=True, max_length=english_tokenized_sequence_length).input_ids
bio_tokens = bio_tokenizer(dna_sequences, return_tensors="pt", padding="max_length", max_length=bio_tokenized_sequence_length, truncation=True).input_ids
bio_tokens = bio_tokens.unsqueeze(0) # 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,
)
``` |