
# 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](https://github.com/cjber/reddit-model).

## 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:

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

## 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 like reading books. I live in Reading.")
```

`out` gives:

```python
[
    {
        "entity_group": "location",
        "score": 0.94123614,
        "word": "reading",
        "start": 32,
        "end": 39,
    }
]

```
{"language": "en", "license": "mit", "datasets": ["wnut_17"], "metrics": ["f1"], "widget": [{"text": "Manchester played Liverpool last night in Liverpool.", "example_title": "Metonyms"}, {"text": "i live in brum - slang for birmingham", "example_title": "Slang / informal text"}]}
transformers pytorch safetensors bert token-classification en dataset:wnut_17 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #token-classification #en #dataset-wnut_17 #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# 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:



## Use in 'transformers'



'out' gives:


# 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: ## Use in 'transformers'



'out' gives:
TAGS
#transformers #pytorch #safetensors #bert #token-classification #en #dataset-wnut_17 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # 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: ## Use in 'transformers'



'out' gives:
passage: TAGS
#transformers #pytorch #safetensors #bert #token-classification #en #dataset-wnut_17 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# 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:## Use in 'transformers'



'out' gives: