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--- |
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library_name: transformers |
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tags: [] |
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--- |
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# ChatNT |
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[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). |
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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. |
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**Developed by:** [InstaDeep](https://huggingface.co/InstaDeepAI) |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [Nucleotide Transformer](https://github.com/instadeepai/nucleotide-transformer) |
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- **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) |
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### How to use |
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Until its next release, the transformers library needs to be installed from source with the following command in order to use the models. |
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PyTorch should also be installed. |
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``` |
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pip install --upgrade git+https://github.com/huggingface/transformers.git |
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pip install torch |
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``` |
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A small snippet of code is given here in order to **generate sequences from a pipeline (high-level)**. |
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``` |
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# Load pipeline |
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from transformers import pipeline |
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pipe = pipeline(model="InstaDeepAI/ChatNT-text-generation-pipeline", trust_remote_code=True) |
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# Define custom inputs (note that the number of <DNA> token in the english sequence must be equal to len(dna_sequences)) |
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english_sequence = "Is there any evidence of an acceptor splice site in this sequence <DNA> ?" |
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dna_sequences = ["ATCGGAAAAAGATCCAGAAAGTTATACCAGGCCAATGGGAATCACCTATTACGTGGATAATAGCGATAGTATGTTACCTATAAATTTAACTACGTGGATATCAGGCAGTTACGTTACCAGTCAAGGAGCACCCAAAACTGTCCAGCAACAAGTTAATTTACCCATGAAGATGTACTGCAAGCCTTGCCAACCAGTTAAAGTAGCTACTCATAAGGTAATAAACAGTAATATCGACTTTTTATCCATTTTGATAATTGATTTATAACAGTCTATAACTGATCGCTCTACATAATCTCTATCAGATTACTATTGACACAAACAGAAACCCCGTTAATTTGTATGATATATTTCCCGGTAAGCTTCGATTTTTAATCCTATCGTGACAATTTGGAATGTAACTTATTTCGTATAGGATAAACTAATTTACACGTTTGAATTCCTAGAATATGGAGAATCTAAAGGTCCTGGCAATGCCATCGGCTTTCAATATTATAATGGACCAAAAGTTACTCTATTAGCTTCCAAAACTTCGCGTGAGTACATTAGAACAGAAGAATAACCTTCAATATCGAGAGAGTTACTATCACTAACTATCCTATG"] |
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# Generate sequence |
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generated_english_sequence = pipe( |
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inputs={ |
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"english_sequence": english_sequence, |
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"dna_sequences": dna_sequences |
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} |
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) |
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``` |
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A small snippet of code is given here in order to **infer with the model without any abstraction (low-level)**. |
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``` |
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import numpy as np |
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from transformers import AutoModel, AutoTokenizer |
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# Load model and tokenizers |
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model = AutoModel.from_pretrained("InstaDeepAI/ChatNT", trust_remote_code=True) |
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english_tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/ChatNT", subfolder="english_tokenizer") |
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bio_tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/ChatNT", subfolder="bio_tokenizer") |
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# Define custom inputs (note that the number of <DNA> token in the english sequence must be equal to len(dna_sequences)) |
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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> ?" |
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dna_sequences = ["ATCGGAAAAAGATCCAGAAAGTTATACCAGGCCAATGGGAATCACCTATTACGTGGATAATAGCGATAGTATGTTACCTATAAATTTAACTACGTGGATATCAGGCAGTTACGTTACCAGTCAAGGAGCACCCAAAACTGTCCAGCAACAAGTTAATTTACCCATGAAGATGTACTGCAAGCCTTGCCAACCAGTTAAAGTAGCTACTCATAAGGTAATAAACAGTAATATCGACTTTTTATCCATTTTGATAATTGATTTATAACAGTCTATAACTGATCGCTCTACATAATCTCTATCAGATTACTATTGACACAAACAGAAACCCCGTTAATTTGTATGATATATTTCCCGGTAAGCTTCGATTTTTAATCCTATCGTGACAATTTGGAATGTAACTTATTTCGTATAGGATAAACTAATTTACACGTTTGAATTCCTAGAATATGGAGAATCTAAAGGTCCTGGCAATGCCATCGGCTTTCAATATTATAATGGACCAAAAGTTACTCTATTAGCTTCCAAAACTTCGCGTGAGTACATTAGAACAGAAGAATAACCTTCAATATCGAGAGAGTTACTATCACTAACTATCCTATG"] |
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# Tokenize |
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english_tokens = english_tokenizer(english_sequence, return_tensors="pt", padding="max_length", truncation=True, max_length=512).input_ids |
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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 |
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# Predict |
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outs = model( |
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multi_omics_tokens_ids=(english_tokens, bio_tokens), |
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projection_english_tokens_ids=english_tokens, |
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projected_bio_embeddings=None, |
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) |
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``` |
