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Upload ESM2 protein model

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  1. .DS_Store +0 -0
  2. README.md +42 -0
  3. config.json +29 -0
  4. model.safetensors +3 -0
  5. special_tokens_map.json +7 -0
  6. tokenizer_config.json +53 -0
  7. vocab.txt +33 -0
.DS_Store ADDED
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README.md ADDED
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+ # ESM2 Protein Model
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+
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+ This is the protein component of a jointly trained NT-ESM2 model pair for DNA-protein analysis.
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+
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+ ## Model Details
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+
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+ - **Model Type**: ESM2 for protein sequences
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+ - **Training**: Jointly trained with NT DNA model
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+ - **Architecture**: Transformer-based language model for proteins
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+
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+ ## Usage
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+
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+ ```python
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+ from transformers import AutoModel, AutoTokenizer
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+
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+ # Load model and tokenizer
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+ model = AutoModel.from_pretrained("vsubasri/joint-nt-esm2-transcript-coding-protein")
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+ tokenizer = AutoTokenizer.from_pretrained("vsubasri/joint-nt-esm2-transcript-coding-protein")
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+
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+ # Example usage
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+ protein_sequence = "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"
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+ inputs = tokenizer(protein_sequence, return_tensors="pt")
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+ outputs = model(**inputs)
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+ ```
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+
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+ ## Training Details
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+
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+ - Jointly trained with DNA sequences for cross-modal understanding
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+ - Large model variant
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+ - Transcript-specific protein coding sequences
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+
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+ ## Files
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+
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+ - `config.json`: Model configuration
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+ - `model.safetensors`: Model weights
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+ - `tokenizer_config.json`: Tokenizer configuration
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+ - `vocab.txt`: Vocabulary file
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+ - `special_tokens_map.json`: Special tokens mapping
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+
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+ ## Citation
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+
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+ If you use this model, please cite the original ESM2 paper and your joint training work.
config.json ADDED
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+ {
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+ "architectures": [
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+ "EsmForMaskedLM"
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+ ],
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+ "layer_norm_eps": 1e-05,
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+ "max_position_embeddings": 1026,
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+ "model_type": "esm",
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+ "num_attention_heads": 20,
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+ "num_hidden_layers": 30,
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+ "pad_token_id": 1,
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+ "position_embedding_type": "rotary",
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+ "token_dropout": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.51.3",
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+ "use_cache": true,
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+ "vocab_list": null,
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+ "vocab_size": 33
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+ }
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