NortheastNER

1
2 languages
license:cc-by-nc-4.0
by
MWirelabs
Other
OTHER
New
1 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
Unknown
Mobile
Laptop
Server
Quick Summary

NortheastNER is a Named Entity Recognition (NER) model fine-tuned by MWirelabs to recognize entities specific to Northeast India.

Code Examples

🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
🚀 Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))

Deploy This Model

Production-ready deployment in minutes

Together.ai

Instant API access to this model

Fastest API

Production-ready inference API. Start free, scale to millions.

Try Free API

Replicate

One-click model deployment

Easiest Setup

Run models in the cloud with simple API. No DevOps required.

Deploy Now

Disclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.