Fill-Mask
Transformers
Safetensors
English
bert
feature-extraction
clinical
healthcare
NLP
BERT
MIMIC-IV
MedNLI
transformer
custom_code
Eval Results (legacy)
Instructions to use Sifal/ClinicalMosaic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sifal/ClinicalMosaic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Sifal/ClinicalMosaic", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Sifal/ClinicalMosaic", trust_remote_code=True) model = AutoModel.from_pretrained("Sifal/ClinicalMosaic", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
File size: 1,040 Bytes
43433dd ff5ca92 43433dd | 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 | # Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
from transformers import BertConfig as TransformersBertConfig
class BertConfig(TransformersBertConfig):
model_type = "clinical_mosaic"
def __init__(
self,
alibi_starting_size: int = 512,
attention_probs_dropout_prob: float = 0.0,
**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__(attention_probs_dropout_prob=attention_probs_dropout_prob, **kwargs)
self.alibi_starting_size = alibi_starting_size |