metadata
language:
- en
tags:
- ner
- gene
- protein
- rna
- bioinfomatics
license: apache-2.0
datasets:
- jnlpba
widget:
- text: >-
It consists of 25 exons encoding a 1,278-amino acid glycoprotein that is
composed of 13 transmembrane domains
NER to find Gene & Gene products
The model was trained on jnlpba dataset, pretrained on this pubmed-pretrained roberta model
All the labels, the possible token classes.
{"label2id": {
"DNA": 2,
"O": 0,
"RNA": 5,
"cell_line": 4,
"cell_type": 3,
"protein": 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-gene-dna-rna-jnlpba-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