metadata

language: en
datasets:
  - wnut_17
license: mit
metrics:
  - f1
widget:
  - text: My name is Sylvain and I live in Paris
    example_title: Parisian
  - text: My name is Sarah and I live in London
    example_title: Londoner

Reddit NER for place names

Fine-tuned bert-base-uncased for named entity recognition, trained using wnut_17 with 498 additional comments from Reddit. This model is intended solely for place name extraction from social media text, other entities have therefore been removed.

This model was created with two key goals:

Improved NER results on social media
Target only place names

In theory this model should be able to detect and ignore metonyms. For example in the sentence:

Manchester played Liverpool last night in London.

Both Manchester and Liverpool refer to football teams, therefore the model outputs:

[ { "entity_group": "location", "score": 0.99784255027771, "word": "london", "start": 42, "end": 48 } ]

Use in `transformers`

from transformers import pipeline

generator = pipeline(
    task="ner",
    model="cjber/reddit-ner-place_names",
    tokenizer="cjber/reddit-ner-place_names",
    aggregation_strategy="first",
)

out = generator("I live north of liverpool in Waterloo")

Out gives:

[{'entity_group': 'location',
  'score': 0.94054973,
  'word': 'liverpool',
  'start': 16,
  'end': 25},
 {'entity_group': 'location',
  'score': 0.99520856,
  'word': 'waterloo',
  'start': 29,
  'end': 37}]

Reddit NER for place names

Use in transformers

Use in `transformers`