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Add Plant DNAGemma model for promoter prediction

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README.md CHANGED
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- ---
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- license: cc-by-nc-sa-4.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: cc-by-nc-sa-4.0
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+ widget:
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+ - text: AAAACATAATAATTTGCCGACTTACTCACCCTGTGATTAATCTATTTTCACTGTGTAGTAAGTAGAGAGTGTTACTTACTACAGTATCTATTTTTGTTTGGATGTTTGCCGTGGACAAGTGCTAACTGTCAAAACCCGTTTTGACCTTAAACCCAGCAATAATAATAATGTAAAACTCCATTGGGCAGTGCAACCTACTCCTCACATATTATATTATAATTCCTAAACCTTGATCAGTTAAATTAATAGCTCTGTTCCCTGTGGCTTTATATAAACACCATGGTTGTCAGCAGTTCAGCA
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+ tags:
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+ - DNA
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+ - biology
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+ - genomics
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+ ---
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+ # Plant foundation DNA large language models
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+
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+ The plant DNA large language models (LLMs) contain a series of foundation models based on different model architectures, which are pre-trained on various plant reference genomes.
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+ All the models have a comparable model size between 90 MB and 150 MB, BPE tokenizer is used for tokenization and 8000 tokens are included in the vocabulary.
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+
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+
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+ **Developed by:** zhangtaolab
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+
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+ ### Model Sources
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+
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+ - **Repository:** [Plant DNA LLMs](https://github.com/zhangtaolab/plant_DNA_LLMs)
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+ - **Manuscript:** [Versatile applications of foundation DNA large language models in plant genomes]()
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+
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+ ### Architecture
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+
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+ The model is trained based on the Google Gemma model with modified config and tokenizer specific for DNA sequence.
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+
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+ This model is fine-tuned for predicting active core promoters.
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+
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+ ### How to use
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+
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+ Install the runtime library first:
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+ ```bash
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+ pip install transformers
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+ ```
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+
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+ Here is a simple code for inference:
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+ ```python
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+ from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
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+
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+ model_name = 'plant-dnagemma-promoter'
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+ # load model and tokenizer
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+ model = AutoModelForSequenceClassification.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
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+ tokenizer = AutoTokenizer.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
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+
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+ # inference
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+ sequences = ['TTACTAAATTTATAACGATTTTTTATCTAACTTTAGCTCATCAATCTTTACCGTGTCAAAATTTAGTGCCAAGAAGCAGACATGGCCCGATGATCTTTTACCCTGTTTTCATAGCTCGCGAGCCGCGACCTGTGTCCAACCTCAACGGTCACTGCAGTCCCAGCACCTCAGCAGCCTGCGCCTGCCATACCCCCTCCCCCACCCACCCACACACACCATCCGGGCCCACGGTGGGACCCAGATGTCATGCGCTGTACGGGCGAGCAACTAGCCCCCACCTCTTCCCAAGAGGCAAAACCT',
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+ 'GACCTAATGATTAACCAAGGAAAAATGCAAGGATTTGACAAAAATATAGAAGCCAATGCTAGGCGCCTAAGTGAATGGATATGAAACAAAAAGCGAGCAGGCTGTCTATATATGGACAATTAGTTGCATTAATATAGTAGTTTATAATTGCAAGCATGGCACTACATCACAACACCTAAAAGACATGCCGTGATGCTAGAACAGCCATTGAATAAATTAGAAAGAAAGGTTGTGGTTAATTAGTTAACGACCAATCGAGCCTACTAGTATAAATTGTACCTCGTTGTTATGAAGTAATTC']
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+ pipe = pipeline('text-classification', model=model, tokenizer=tokenizer,
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+ trust_remote_code=True, top_k=None)
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+ results = pipe(sequences)
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+ print(results)
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+
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+ ```
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+
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+
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+ ### Training data
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+ We use GemmaForSequenceClassification to fine-tune the model.
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+ Detailed training procedure can be found in our manuscript.
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+
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+
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+ #### Hardware
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+ Model was trained on a NVIDIA GTX1080Ti GPU (11 GB).
config.json ADDED
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+ {
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+ "_name_or_path": "Plant_DNAGemma_promoter",
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+ "architectures": [
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+ "GemmaForSequenceClassification"
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+ ],
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
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+ "bos_token_id": 2,
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+ "eos_token_id": 1,
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+ "head_dim": 256,
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+ "hidden_act": "gelu_pytorch_tanh",
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+ "hidden_activation": "gelu_pytorch_tanh",
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "Not promoter",
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+ "1": "Core promoter"
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+ },
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "label2id": {
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+ "Not promoter": 0,
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+ "Core promoter": 1
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+ },
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+ "max_position_embeddings": 1024,
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+ "rms_norm_eps": 1e-06,
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+ "rope_theta": 10000.0,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.41.2",
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+ "use_cache": true,
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+ "vocab_size": 8002
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+ }
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