Feature Extraction
Transformers
Safetensors
English
porebert
nanopore
dna
sequencing
genomics
bioinformatics
foundation-model
transformer
bert
masked-language-modeling
signal-processing
vector-quantization
vq-tokenizer
custom_code
Instructions to use ShuaiAnwo/PoreBERT-DNA-VQI-007M-526 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShuaiAnwo/PoreBERT-DNA-VQI-007M-526 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ShuaiAnwo/PoreBERT-DNA-VQI-007M-526", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ShuaiAnwo/PoreBERT-DNA-VQI-007M-526", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "transformers_version": "5.5.4", | |
| "architectures": null, | |
| "output_hidden_states": false, | |
| "return_dict": true, | |
| "dtype": null, | |
| "chunk_size_feed_forward": 0, | |
| "is_encoder_decoder": false, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1" | |
| }, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1 | |
| }, | |
| "problem_type": null, | |
| "_name_or_path": "", | |
| "pad_token_id": 0, | |
| "intermediate_size": 1024, | |
| "vocab_size": 2560, | |
| "hidden_size": 256, | |
| "num_hidden_layers": 8, | |
| "num_attention_heads": 8, | |
| "max_position_embeddings": 1536, | |
| "dropout": 0.1, | |
| "initializer_range": 0.02, | |
| "gradient_checkpointing": true, | |
| "model_type": "porebert", | |
| "output_attentions": false, | |
| "auto_map": { | |
| "AutoConfig": "configuration_simple_bert.SimpleBERTConfig", | |
| "AutoModel": "modeling_simple_bert.SimpleBERTModel" | |
| } | |
| } |