Instructions to use Taykhoom/DNABERT-4mer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taykhoom/DNABERT-4mer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Taykhoom/DNABERT-4mer", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Taykhoom/DNABERT-4mer", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Taykhoom/DNABERT-4mer: direct link, hf CLI and curl.
- Browser
- Download file 670 Bytes
-
https://huggingface.co/Taykhoom/DNABERT-4mer/resolve/main/config.json
- Command line
-
hf download hf://Taykhoom/DNABERT-4mer/config.json
-
curl -L -o config.json https://huggingface.co/Taykhoom/DNABERT-4mer/resolve/main/config.json
670 Bytes
| { | |
| "architectures": [ | |
| "BertForMaskedLM" | |
| ], | |
| "model_type": "bert_updated", | |
| "auto_map": { | |
| "AutoConfig": "configuration_bert_updated.BertUpdatedConfig", | |
| "AutoModel": "modeling_bert.BertModel", | |
| "AutoModelForMaskedLM": "modeling_bert.BertForMaskedLM" | |
| }, | |
| "vocab_size": 261, | |
| "hidden_size": 768, | |
| "num_hidden_layers": 12, | |
| "num_attention_heads": 12, | |
| "intermediate_size": 3072, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "attention_probs_dropout_prob": 0.1, | |
| "max_position_embeddings": 512, | |
| "type_vocab_size": 2, | |
| "initializer_range": 0.02, | |
| "layer_norm_eps": 1e-12, | |
| "pad_token_id": 0, | |
| "kmer": 4, | |
| "model_max_length": 512 | |
| } | |