Feature Extraction
Transformers
Joblib
Safetensors
BulkRNABert
bulk RNA-seq
biology
transcriptomics
custom_code
Instructions to use InstaDeepAI/BulkRNABert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InstaDeepAI/BulkRNABert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="InstaDeepAI/BulkRNABert", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("InstaDeepAI/BulkRNABert", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "BulkRNABert" | |
| ], | |
| "attention_maps_to_save": [], | |
| "auto_map": { | |
| "AutoConfig": "bulkrnabert.BulkRNABertConfig", | |
| "AutoModel": "bulkrnabert.BulkRNABert" | |
| }, | |
| "embed_dim": 256, | |
| "embeddings_layers_to_save": [], | |
| "ffn_embed_dim": 512, | |
| "init_gene_embed_dim": 200, | |
| "key_size": 32, | |
| "model_type": "BulkRNABert", | |
| "n_expressions_bins": 66, | |
| "n_genes": 19062, | |
| "num_attention_heads": 8, | |
| "num_layers": 4, | |
| "project_gene_embedding": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.51.0", | |
| "use_gene_embedding": true | |
| } | |