Upload 6 files
Browse files- README.md +57 -0
- config.json +49 -0
- model.safetensors +3 -0
- special_tokens_map.json +37 -0
- tokenizer_config.json +52 -0
- vocab.txt +11 -0
README.md
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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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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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**Developed by:** zhangtaolab
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### Model Sources
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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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### Architecture
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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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This model is fine-tuned for predicting H3K27me3 histone modification.
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### How to use
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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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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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### 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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#### Hardware
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Model was trained on a NVIDIA GTX1080Ti GPU (11 GB).
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config.json
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{
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"_name_or_path": "Plant_DNAGPT_singlebase_H3K27me3",
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"activation_function": "gelu_new",
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"architectures": [
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"GPT2ForSequenceClassification"
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],
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"attn_pdrop": 0.1,
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"bos_token_id": 10,
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"embd_pdrop": 0.1,
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"eos_token_id": 9,
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"id2label": {
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"0": "Not H3K27me3",
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"1": "H3K27me3"
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},
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"initializer_range": 0.02,
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"label2id": {
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"Not H3K27me3": 0,
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"H3K27me3": 1
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},
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt2",
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"n_ctx": 512,
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"n_embd": 768,
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"n_head": 12,
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"n_inner": null,
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"n_layer": 12,
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"n_positions": 1024,
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"pad_token_id": 1,
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"problem_type": "single_label_classification",
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"reorder_and_upcast_attn": false,
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"resid_pdrop": 0.1,
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"scale_attn_by_inverse_layer_idx": false,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"task_specific_params": {
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"text-generation": {
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"do_sample": true,
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"max_length": 50
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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.42.2",
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"use_cache": true,
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"vocab_size": 11
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:fd35733302012c227ee268a9e3ee4169697f7915a1774b5e7df12cca4bbdec89
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size 343424712
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special_tokens_map.json
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{
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"cls_token": {
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"content": "<cls>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "<eos>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"mask_token": {
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"content": "<mask>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<pad>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "<pad>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "<mask>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"3": {
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"content": "<cls>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"9": {
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"content": "<eos>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"clean_up_tokenization_spaces": true,
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"cls_token": "<cls>",
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"eos_token": "<eos>",
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"mask_token": "<mask>",
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"model_max_length": 512,
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"pad_token": "<pad>",
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"tokenizer_class": "EsmTokenizer",
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"unk_token": "<unk>"
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}
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vocab.txt
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<unk>
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<pad>
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<mask>
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<cls>
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A
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T
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C
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G
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N
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<eos>
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<bos>
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