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---
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:
1. Improved NER results on social media
2. 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`
```python
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:
```python
[{'entity_group': 'location',
'score': 0.94054973,
'word': 'liverpool',
'start': 16,
'end': 25},
{'entity_group': 'location',
'score': 0.99520856,
'word': 'waterloo',
'start': 29,
'end': 37}]
``` |