Add Plant DNAGPT 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 OpenAI GPT-2 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-dnagpt-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 GPT2ForSequenceClassification 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).

added_tokens.json

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+{
+  "<|endoftext|>": 8000,
+  "[PAD]": 8001
+}

config.json

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+{
+  "_name_or_path": "Plant_DNAGPT_promoter_strength_protoplast",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2ForSequenceClassification"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 8000,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 8000,
+  "id2label": {
+    "0": "Promoter strength in tobacco leaves"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "Promoter strength in tobacco leaves": 0
+  },
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 512,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "pad_token_id": 8001,
+  "problem_type": "regression",
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "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 sha256:7414c07f1f9e6d72c2ec7d3614ac9227daf970eca65fb8be19af8bba26d4a84c
+size 367969992

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "8000": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "8001": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "stride": 0,
+  "tokenizer_class": "GPT2Tokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<|endoftext|>"
+}