Add Plant DNAGemma model for promoter strength in protoplast prediction

README.md

@@ -1,3 +1,63 @@
----
-license: cc-by-nc-sa-4.0
----
+---
+license: cc-by-nc-sa-4.0
+widget:
+- text: AGTCCAGTGGACGACCAGCCACGGCTCCGGTCTGTAGAACCATCGCGGAAACGGCTCGCAAAACTCTAAACAGCGCAAACGATGCGCGCGCCGAAGCAACCCGGCTCTACTTATAAAAACGTCCAACGGTGAGCACCGAGCAGCTACTACTCGTACTCCCCCCACCGATC
+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 large language models in plant genomes]() 
+
+### Architecture
+
+The model is trained based on the Google Gemma model with modified tokenizer specific for DNA sequence.
+
+This model is fine-tuned for predicting promoter strength in maize protoplasts system.
+
+
+### 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-dnagemma-promoter_strength_protoplast'
+# 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 = ['TACTCTAATCGTATCAGCTGCACTTGCGTACAGGCTACCGGCGTCCTCAGCCACGTAAGAAAAGGCCCAATAAAGGCCCAACTACAACCAGCGGATATATATACTGGAGCCTGGCGAGATCACCCTAACCCCTCACACTCCCATCCAGCCGCCACCAGGTGCAGAGTGTT',
+             'ATTTCAAAACTAGTTTTCTATAAACGAAAACTTATATTTATTCCGCTTGTTCCGTTTGATCTGCTGATTCGACACCGTTTTAACGTATTTTAAGTAAGTATCAGAAATATTAATGTGAAGATAAAAGAAAATAGAGTAAATGTAAAGGAAAATGCATAAGATTTTGTTGA']
+pipe = pipeline('text-classification', model=model, tokenizer=tokenizer,
+                trust_remote_code=True, function_to_apply="none")
+results = pipe(sequences)
+print(results)
+
+```
+
+
+### Training data
+We use GemmaForSequenceClassification 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": "Plant_DNAGemma_promoter_strength_protoplast",
+  "architectures": [
+    "GemmaForSequenceClassification"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 2,
+  "eos_token_id": 1,
+  "head_dim": 256,
+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Promoter strength in tobacco leaves"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Promoter strength in tobacco leaves": 0
+  },
+  "max_position_embeddings": 1024,
+  "model_type": "gemma",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "problem_type": "regression",
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.1",
+  "use_cache": true,
+  "vocab_size": 8002
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ed4a762b65b0972f43dd3da9531cc4ac07bc56c736c182bbec6e50acd9c2a861
+size 609779736

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<eos>",
+    "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
+  }
+}

tokenizer_config.json

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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<eos>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<bos>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<bos>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<eos>",
+  "legacy": null,
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "GemmaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}