metadata
language:
- en
tags:
- ner
- ncbi
- disease
- pubmed
- bioinfomatics
license: apache-2.0
datasets:
- ncbi-disease
- bc5cdr
widget:
- text: >-
Hepatocyte nuclear factor 4 alpha (HNF4α) is regulated by different
promoters to generate two isoforms, one of which functions as a tumor
suppressor. Here, the authors reveal that induction of the alternative
isoform in hepatocellular carcinoma inhibits the circadian clock by
repressing BMAL1, and the reintroduction of BMAL1 prevents HCC tumor
growth.
NER to find Gene & Gene products
The model was trained on ncbi-disease, BC5CDR dataset, pretrained on this pubmed-pretrained roberta model All the labels, the possible token classes.
{"label2id": {
"O": 0,
"Disease":1,
}
}
Notice, we removed the 'B-','I-' etc from data label.🗡
This is the template we suggest for using the model
from transformers import pipeline
PRETRAINED = "raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed"
ner = pipeline(task="ner",model=PRETRAINED, tokenizer=PRETRAINED)
ner("Your text", aggregation_strategy="first")
And here is to make your output more consecutive ⭐️
import pandas as pd
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(PRETRAINED)
def clean_output(outputs):
results = []
current = []
last_idx = 0
# make to sub group by position
for output in outputs:
if output["index"]-1==last_idx:
current.append(output)
else:
results.append(current)
current = [output, ]
last_idx = output["index"]
if len(current)>0:
results.append(current)
# from tokens to string
strings = []
for c in results:
tokens = []
starts = []
ends = []
for o in c:
tokens.append(o['word'])
starts.append(o['start'])
ends.append(o['end'])
new_str = tokenizer.convert_tokens_to_string(tokens)
if new_str!='':
strings.append(dict(
word=new_str,
start = min(starts),
end = max(ends),
entity = c[0]['entity']
))
return strings
def entity_table(pipeline, **pipeline_kw):
if "aggregation_strategy" not in pipeline_kw:
pipeline_kw["aggregation_strategy"] = "first"
def create_table(text):
return pd.DataFrame(
clean_output(
pipeline(text, **pipeline_kw)
)
)
return create_table
# will return a dataframe
entity_table(ner)(YOUR_VERY_CONTENTFUL_TEXT)
check our NER model on