Feature Extraction
Transformers
Safetensors
Upper Grand Valley Dani
bert
DNA
BERT
language-model
genomics
custom_code
text-embeddings-inference
Instructions to use Taykhoom/DNABERT-S with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taykhoom/DNABERT-S with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Taykhoom/DNABERT-S", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Taykhoom/DNABERT-S", trust_remote_code=True) model = AutoModel.from_pretrained("Taykhoom/DNABERT-S", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
File size: 954 Bytes
2b4d944 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"_name_or_path": "Taykhoom/DNABERT-S",
"alibi_starting_size": 512,
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.0,
"auto_map": {
"AutoConfig": "Taykhoom/MosaicBERT-updated--configuration_bert.BertConfig",
"AutoModel": "Taykhoom/MosaicBERT-updated--bert_layers.BertModel",
"AutoModelForMaskedLM": "Taykhoom/MosaicBERT-updated--bert_layers.BertForMaskedLM",
"AutoModelForSequenceClassification": "Taykhoom/MosaicBERT-updated--bert_layers.BertForSequenceClassification"
},
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"model_max_length": 10000,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 3,
"transformers_version": "4.57.6",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 4096
}
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