Instructions to use zhihan1996/DNA_bert_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zhihan1996/DNA_bert_6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="zhihan1996/DNA_bert_6", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("zhihan1996/DNA_bert_6", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("zhihan1996/DNA_bert_6", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 807 Bytes
3183ac4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | # Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
from transformers import BertConfig as TransformersBertConfig
class BertConfig(TransformersBertConfig):
def __init__(
self,
**kwargs,
):
"""Configuration class for MosaicBert.
Args:
alibi_starting_size (int): Use `alibi_starting_size` to determine how large of an alibi tensor to
create when initializing the model. You should be able to ignore this parameter in most cases.
Defaults to 512.
attention_probs_dropout_prob (float): By default, turn off attention dropout in Mosaic BERT
(otherwise, Flash Attention will be off by default). Defaults to 0.0.
"""
super().__init__(**kwargs)
|