Update README.md

README.md

@@ -3,4 +3,42 @@ library_name: transformers
 tags: []
 ---
 
-# The model is currently being tested, it should not be used for now
+### 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,
+)
+```