Feature Extraction
Transformers
Safetensors
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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| # Copyright 2022 MosaicML Examples authors | |
| # SPDX-License-Identifier: Apache-2.0 | |
| from transformers import BertConfig as TransformersBertConfig | |
| class BertConfig(TransformersBertConfig): | |
| auto_map = { | |
| "AutoConfig": "configuration_bert.BertConfig", | |
| "AutoModel": "bert_layers.BertModel", | |
| "AutoModelForMaskedLM": "bert_layers.BertForMaskedLM", | |
| "AutoModelForSequenceClassification": "bert_layers.BertForSequenceClassification", | |
| } | |
| def __init__( | |
| self, | |
| alibi_starting_size: int = 1024, | |
| attention_probs_dropout_prob: float = 0.0, | |
| **kwargs, | |
| ): | |
| super().__init__( | |
| attention_probs_dropout_prob=attention_probs_dropout_prob, | |
| **kwargs, | |
| ) | |
| self.alibi_starting_size = alibi_starting_size | |