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  1. README.md +57 -0
  2. config.json +49 -0
  3. model.safetensors +3 -0
  4. special_tokens_map.json +37 -0
  5. tokenizer_config.json +52 -0
  6. vocab.txt +11 -0
README.md CHANGED
@@ -1,3 +1,60 @@
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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: AATTTTAACTAGCCCCTTCGGCCCTTCCCATCGACATATATACGAAGAGACAAAACAACATATCAACAGAATGTCAGAATTACAGACACCACGCTTGACATGTCTGTGACGCAGACCATAGAGGATGTGTCATGTTCATGTGTCCAATGGGGGCAATGGTATTGCAAGGGCACAAAATACTGCTAACATGTTTCGTAGCGCTATAGGTTACAGAGGTCATGACGTTAT
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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 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 OpenAI GPT-2 model with modified tokenizer specific for DNA sequence.
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+
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+ This model is fine-tuned for predicting H3K27me3 histone modification.
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+
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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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+ model_name = 'plant-dnagpt-singlebase-H3K27me3'
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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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+ # inference
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+ sequences = ['ATCTTTTAAACCCTACTTTTCTTCACATTATTCATAATAGGCACTCTCAACTCATGGTTTAGTGGAGTTACACAATACCCAAGGTTGGGTCAAGGCCAAGACGTGATTGGTTTCTTCATTGGGCACCCTCAACTTCTGATTTTGTCCTAAGTTGAGGTAAACATGTGCAAATCTTGAATCTCCAACACCACCCGACGGAAAACTCTTCCTTTTGCCTAACGCTTTTGCTTAGCGATTGTATATGT',
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+ 'GCATAATCGAGCTTGATGCCCATGTTTTTGCACCAGAGTTTTACCTCGTCGGCCGTAAAGTTCGTGCCGTTATCAGTGATGATGTTGTGGGGGACGCCGTAACAGTGTACAACCCCGGATATAAAGTCTATCACCGGTCCAGATTCGGCCGTCTCAACAGGCTTGGCTTCTATCCATTTGGT']
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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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+ ### Training data
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+ We use GPT2ForSequenceClassification 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).
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+ }
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