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
Upload 2 files
Browse files- jax_params/config.json +1 -0
- jax_params/params.joblib +3 -0
jax_params/config.json
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{"n_genes": 19062, "n_expressions_bins": 64, "embed_dim": 256, "init_gene_embed_dim": 200, "project_gene_embedding": true, "use_gene_embedding": true, "num_attention_heads": 8, "key_size": 32, "ffn_embed_dim": 512, "num_layers": 4, "use_memory_efficient_attention": false, "use_gradient_checkpointing": true, "gene2vec_weights_path": "data/gene2vec_weights_common_gene_ids.npy", "embeddings_layers_to_save": [], "attention_layers_to_save": [], "use_log_normalization": true, "use_max_normalization": true, "normalization_factor": 5.547176906585117}
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jax_params/params.joblib
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