AttributeError: 'list' object has no attribute 'ents' in building NER using BERT

Question

I’m trying to build a NER model using Bert-base-NER for a tweets dataset and ending up getting this error . Please help

This is what I have done

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)

# ---------

def all_ents(v):
        return [(ent.text, ent.label_) for ent in nlp(v).ents]

df1['Entities'] = df['text'].apply(lambda v: all_ents(v))

df1.head()

AttributeError: 'list' object has no attribute 'ents'

Thank you for the help

Asked By: d_stupido_02

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Source

Answer 1

It seems you mix code from different modules.

.ents exists in module spacy but not in transformers

#import spacy
import en_core_web_sm

nlp = en_core_web_sm.load()

doc = nlp('Hello World of Python. Have a nice day')

print([(x.text, x.label_) for x in doc.ents])

In transformers you should use directly nlp(v) but it gives directory with ent["entity"], ent["score"], ent["index"], ent["word"], ent["start"], ent["end"]

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)

# ---------

import pandas as pd

df = pd.DataFrame({
    'text': ['Hello World of Python. Have a nice day']
})

# ---------

def all_ents(v):
    #print(nlp(v))
    return [(ent['word'], ent['entity']) for ent in nlp(v)]

df['Entities'] = df['text'].apply(all_ents)

#df1['Entities'] = df['text'].apply(lambda v: [(ent['word'], ent['entity']) for ent in nlp(v)])

print(df['Entities'].head())

Answered By: furas

AttributeError: 'list' object has no attribute 'ents' in building NER using BERT

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