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@@ -1,11 +1,18 @@
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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: ATTTTGGAAATCGGAAATCTTCTGTGTTTATCCGTACCAATCCCTGGATCAGTAGTTGGTACACACATACATAGTTGGATCACAACATATCACGAATGAATTTAGGCTAAAAGAGTTAAATACTTACATTAGGGCCAGGTAGGCCAACAACTATCAGAGAACAACAGCGGAAGACAAAATAATATAAGGGCCCGGTTAACATGCCACAAGCAGTCGACTGGGGAACGAGACCTAGAACAAGACCGCACTCCGATCATCTTGTGGGATACGCAAGCGTACCGACAAGGGCTTCTCTTCAACACTCTCCTAAAAGATATATAAATAGCAAGGGTGAGTACCAACCGTACTCAGCAAGCCACCACAACAACAATGCGTATGATAGAGGGTATTTCAAGGAATGGCTTCAGGTTCTTTTGCATAAAGCTAATTTTACAATTCTTTTCACAAGCCTAAAACCTAGCATAGACTGATCAAATTTTAGTACCAGTGTTCACTTTAAACAACGACGGTTCTGTCCACCATCCATTGTGATCCCAAGGATAGCTTCCCGCCATTGAATCGTCATGGTTTTCTAAGGATGTCCACCTTCCCTCCTCTCGGGAAGTGGCTCCATCAGCATAAAATTCATCATGCAATATCCCATCCCCCACAAGTTAAAAATTTAGAGTCTAGCCAAGTGTAATACATGTCCCGGTGCTCAATAACCGCGAGCACGGCTATTCGAATAGATTTGGTTTACTCACACTGCAGTGGATGTACACTTTACCCGCACTCCGCAACTGCCCAACACATGAGCCTCGTCCGAACACATGGGACGCGTCACGGCAAAGCTTTTCGATAACCTCGCATTGGTAGTACCCGCTCCATGAACTTAAATCCTCATGCACTCTAGGCGTCCATGTTTCTAGCAGTGAGAGGAGTTCTGGCGCTCCCGGGAAAGAGAAGTCTCACACGCATATTAAATTATGGTTCAAGTTAAGTTCTCTCTCTCACACACTCA
 
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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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@@ -37,7 +44,7 @@ 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-conservation'
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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)
@@ -59,4 +66,4 @@ Detailed training procedure can be found in our manuscript.
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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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  license: cc-by-nc-sa-4.0
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  widget:
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+ - text: >-
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+ ATTTTGGAAATCGGAAATCTTCTGTGTTTATCCGTACCAATCCCTGGATCAGTAGTTGGTACACACATACATAGTTGGATCACAACATATCACGAATGAATTTAGGCTAAAAGAGTTAAATACTTACATTAGGGCCAGGTAGGCCAACAACTATCAGAGAACAACAGCGGAAGACAAAATAATATAAGGGCCCGGTTAACATGCCACAAGCAGTCGACTGGGGAACGAGACCTAGAACAAGACCGCACTCCGATCATCTTGTGGGATACGCAAGCGTACCGACAAGGGCTTCTCTTCAACACTCTCCTAAAAGATATATAAATAGCAAGGGTGAGTACCAACCGTACTCAGCAAGCCACCACAACAACAATGCGTATGATAGAGGGTATTTCAAGGAATGGCTTCAGGTTCTTTTGCATAAAGCTAATTTTACAATTCTTTTCACAAGCCTAAAACCTAGCATAGACTGATCAAATTTTAGTACCAGTGTTCACTTTAAACAACGACGGTTCTGTCCACCATCCATTGTGATCCCAAGGATAGCTTCCCGCCATTGAATCGTCATGGTTTTCTAAGGATGTCCACCTTCCCTCCTCTCGGGAAGTGGCTCCATCAGCATAAAATTCATCATGCAATATCCCATCCCCCACAAGTTAAAAATTTAGAGTCTAGCCAAGTGTAATACATGTCCCGGTGCTCAATAACCGCGAGCACGGCTATTCGAATAGATTTGGTTTACTCACACTGCAGTGGATGTACACTTTACCCGCACTCCGCAACTGCCCAACACATGAGCCTCGTCCGAACACATGGGACGCGTCACGGCAAAGCTTTTCGATAACCTCGCATTGGTAGTACCCGCTCCATGAACTTAAATCCTCATGCACTCTAGGCGTCCATGTTTCTAGCAGTGAGAGGAGTTCTGGCGCTCCCGGGAAAGAGAAGTCTCACACGCATATTAAATTATGGTTCAAGTTAAGTTCTCTCTCTCACACACTCA
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  tags:
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  - DNA
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  - biology
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  - genomics
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+ datasets:
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+ - zhangtaolab/plant-multi-species-sequence-conservation
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+ metrics:
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+ - accuracy
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+ base_model:
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+ - zhangtaolab/plant-dnagpt-BPE
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  ---
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  # Plant foundation DNA large language models
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  ```python
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  from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
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+ model_name = 'plant-dnagpt-BPE-conservation'
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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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  #### Hardware
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+ Model was trained on a NVIDIA GTX1080Ti GPU (11 GB).