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README.md

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----
-license: cc-by-nc-sa-4.0
----
+---
+license: cc-by-nc-sa-4.0
+widget:
+- text: AGTCGCCGCAACCCACACACGGACGGCTCGACGTGGCGATCTTAGCGGCTCATCCGCCCGGCCTCCCTCGCGCTCGATCGCTACGCAGCCTACGCTCGTTTCGCTCGGTTCGGTGGGTCGCCGATCTGGCGCCACGGCGGCTACCAACGACACCGCGATTGAGAAGGGTGCGTGGCCGTGGAGTCGTGGAGAAACGCCCGCGCGCGCGGGTGCGGCGAGGGACGACGACCGCGTCGTGCGGATCGATTGGCGGGGCAGCTCGGCGCCCCG
+tags:
+- DNA
+- biology
+- genomics
+---
+# Plant foundation DNA large language models
+
+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.  
+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.  
+
+
+**Developed by:** zhangtaolab
+
+### Model Sources
+
+- **Repository:** [Plant DNA LLMs](https://github.com/zhangtaolab/plant_DNA_LLMs)
+- **Manuscript:** [Versatile applications of foundation DNA language models in plant genomes]() 
+
+### Architecture
+
+The model is trained based on the zhihan1996/DNABERT-2-117M model with modified tokenizer.
+
+This model is fine-tuned for predicting H3K27ac histone modification.
+
+
+### How to use
+
+Install the runtime library first:
+```bash
+pip install transformers
+```
+
+Here is a simple code for inference:
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+
+model_name = 'plant-dnabert-singlebase-H3K27ac'
+# load model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
+
+# inference
+sequences = ['GCTTTGGTTTATACCTTACACAACATAAATCACATAGTTAATCCCTAATCGTCTTTGATTCTCAATGTTTTGTTCATTTTTACCATGAACATCATCTGATTGATAAGTGCATAGAGAATTAACGGCTTACACTTTACACTTGCATAGATGATTCCTAAGTATGTCCT',
+             'TAGCCCCCTCCTCTCTTTATATAGTGCAATCTAATATATGAAAGGTTCGGTGATGGGGCCAATAAGTGTATTTAGGCTAGGCCTTCATGGGCCAAGCCCAAAAGTTTCTCAACACTCCCCCTTGAGCACTCACCGCGTAATGTCCATGCCTCGTCAAAACTCCATAAAAACCCAGTG']
+pipe = pipeline('text-classification', model=model, tokenizer=tokenizer,
+                trust_remote_code=True, top_k=None)
+results = pipe(sequences)
+print(results)
+
+```
+
+
+### Training data
+We use BertForSequenceClassification to fine-tune the model.  
+Detailed training procedure can be found in our manuscript.
+
+
+#### Hardware
+Model was trained on a NVIDIA GTX1080Ti GPU (11 GB).

config.json

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+{
+  "_name_or_path": "../model/PlantDna_BERT_1mer",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Not_modification",
+    "1": "modification"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Not_modification": 0,
+    "modification": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 11
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:06b03d1904e77f7cba040a705e38084124fcc48daa2c1688fd29037a3161ff66
+size 344228720

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "<cls>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

test_metrics.json

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+{'test_loss': 0.541134774684906, 'test_accuracy': 0.73662109375, 'test_f1': 0.7403984984117817, 'test_precision': 0.7299297779464794, 'test_recall': 0.751171875, 'test_matthews_correlation': 0.4734427095024038, 'test_runtime': 34.2049, 'test_samples_per_second': 299.373, 'test_steps_per_second': 18.711}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<mask>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<cls>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<cls>",
+  "eos_token": null,
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "tokenizer_class": "EsmTokenizer",
+  "unk_token": "<unk>"
+}

training_args.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d1aef61a1e00541d10830360e32f3ff0fc339436aeaf72a67f45fb840b48e283
+size 5432

vocab.txt

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+<unk>
+<pad>
+<mask>
+<cls>
+A
+T
+C
+G
+N
+<eos>
+<bos>