Update README.md

README.md

@@ -8,7 +8,6 @@ pipeline_tag: text-generation
 [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 and it generalizes across multiple biological tasks and modalities.
 
-
 **Developed by:** [InstaDeep](https://huggingface.co/InstaDeepAI)
 
 ### Model Sources
@@ -19,7 +18,34 @@ It enables users — even those with no coding background — to interact with b
 - **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
+### Architecture and Parameters  
+ChatNT is built on a three‑module design: a 500M‑parameter [Nucleotide Transformer v2](https://www.nature.com/articles/s41592-024-02523-z) DNA encoder pre‑trained on genomes from 850 species 
+(handling up to 12 kb per sequence, Dalla‑Torre et al., 2024), an English‑aware Perceiver Resampler that linearly projects and gated‑attention compresses 
+2048 DNA‑token embeddings into 64 task‑conditioned vectors (REF), and a frozen 7B‑parameter [Vicuna‑7B](https://lmsys.org/blog/2023-03-30-vicuna/) decoder.
+
+Users provide a natural‑language prompt containing one or more `<DNA>` placeholders and the corresponding DNA sequences (tokenized as 6‑mers). 
+The projection layer inserts 64 resampled DNA embeddings at each placeholder, and the Vicuna decoder generates free‑form English responses in 
+an autoregressive fashion, using low‑temperature sampling to produce classification labels, multi‑label statements, or numeric values.
+
+### Training Data  
+ChatNT was instruction‑tuned on a unified corpus covering 27 diverse tasks from DNA, RNA and proteins, spanning multiple species, tissues and biological processes. 
+This amounted to 605 million DNA tokens (≈ 3.6 billion bases) and 273 million English tokens, sampled uniformly over tasks for 2 billion instruction tokens.
+
+### Tokenization  
+DNA inputs are broken into overlapping 6‑mer tokens and padded or truncated to 2048 tokens (~ 12 kb). English prompts and 
+outputs use the LLaMA tokenizer, augmented with `<DNA>` as a special token to mark sequence insertion points.
+
+### Credit and License  
+The DNA encoder is the Nucleotide Transformer v2 ([Dalla‑Torre et al., 2024](https://www.nature.com/articles/s41592-024-02523-z)), and the English decoder is Vicuna‑7B (
+[Chiang et al., 2023](https://lmsys.org/blog/2023-03-30-vicuna/)). All code and model artifacts are released under ???.
+
+### Limitations and Disclaimer  
+While ChatNT excels at conversational molecular‑phenotype tasks, it is **not** a clinical or diagnostic tool. It can produce incorrect or 
+“hallucinated” answers, particularly on out‑of‑distribution inputs, and its numeric predictions may suffer digit‑level errors. Confidence 
+estimates require post‑hoc calibration. Users should always validate critical outputs against experiments or specialized bioinformatics 
+pipelines.
+
+## 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.
@@ -30,6 +56,9 @@ pip install torch
 ```
 
 A small snippet of code is given here in order to **generate ChatNT answers from a pipeline (high-level)**.
+- The prompt used for training ChatNT is already incorporated inside the pipeline and is the following:
+  "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."
 
 ```
 # Load pipeline
@@ -63,6 +92,7 @@ english_tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/ChatNT", subfolde
 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))
+# Here the english sequence should include the prompt
 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"]