Edit model card

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

Model code

For the model code please see the following Model GitHub Repository.

Metonymy

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

Manchester played Liverpool last night in Liverpool.

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

[
    {
        "entity_group": "location",
        "score": 0.9975672,
        "word": "liverpool",
        "start": 42,
        "end": 51,
    }
]

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 like reading books. I live in Reading.")

out gives:

[
    {
        "entity_group": "location",
        "score": 0.94123614,
        "word": "reading",
        "start": 32,
        "end": 39,
    }
]
Downloads last month
104
Safetensors
Model size
109M params
Tensor type
I64
·
F32
·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for cjber/reddit-ner-place_names

Finetunes
1 model

Dataset used to train cjber/reddit-ner-place_names