---
tags:
- flair
- token-classification
- sequence-tagger-model
language: 
- id
---

## English NER in Flair (default model)

This is the POS model for Indonesian that ships with [Flair](https://github.com/flairNLP/flair/). The architecture of this model uses **FastText**.

- F-score (micro) = **0.9345**
- F-score (macro) = **0.8735**
- Accuracy = **0.9345**

Predicts 19 tags:

| **Tag**  | **Meaning**                       |
|----------|-----------------------------------|
| NOUN     | Noun (person, place, thing, or idea) |
| PROPN    | Proper noun (specific name)       |
| PUNCT    | Punctuation (marks like commas, periods, etc.) |
| VERB     | Verb (action or state)            |
| ADP      | Adposition (prepositions or postpositions) |
| PRON     | Pronoun (substitute for a noun)   |
| ADJ      | Adjective (describes a noun)      |
| NUM      | Numeral (number or quantity)     |
| DET      | Determiner (a word that modifies a noun) |
| CCONJ    | Coordinating conjunction (joins clauses or words) |
| ADV      | Adverb (modifies a verb, adjective, or another adverb) |
| AUX      | Auxiliary verb (helps the main verb) |
| SCONJ    | Subordinating conjunction (introduces subordinate clauses) |
| PART     | Particle (small word that doesn’t change in form, e.g., "not") |
| SYM      | Symbol (mathematical or other special symbols) |
| X        | Other (words that don't fit standard POS categories) |
| INTJ     | Interjection (expresses strong emotion or reaction) |

---

### Demo: How to use in Flair

Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`).

You also need to download the **model** file locally to use it.

You can find training or fine-tuning code here : https://github.com/bwbayu/product_name_clustering/blob/main/additional/train_pos_flair.ipynb

```python
from flair.data import Sentence
from flair.models import SequenceTagger

tagger = SequenceTagger.load("model")
text = "aku pergi ke pasar"
sentence = Sentence(text)
tagger.predict(sentence)
for token in sentence:
    print(f"{token.text} ({token.get_label('upos').value})")

```

This yields the following output:
```
aku (PRON)
pergi (VERB)
ke (ADP)
pasar (NOUN)
```

---

### Cite

Please cite the following paper when using this model.

```
@inproceedings{akbik2018coling,
  title={Contextual String Embeddings for Sequence Labeling},
  author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
  booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
  pages     = {1638--1649},
  year      = {2018}
}
```

---

### Issues?

The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).