bert-spanish-cased-finetuned-ner

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Edge AI:
Mobile
Laptop
Server
1GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
1GB+ RAM

Code Examples

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B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
text
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Model in actionpythontransformers
from transformers import pipeline

nlp_ner = pipeline(
    "ner",
    model="mrm8488/bert-spanish-cased-finetuned-ner",
    tokenizer=(
        'mrm8488/bert-spanish-cased-finetuned-ner',  
        {"use_fast": False}
))

text = 'Mis amigos están pensando viajar a Londres este verano'

nlp_ner(text)

#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]

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