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README.md

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+---
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+language: 
+- id
+- en
+---
+
+## English NER in Flair (default model)
+
+This is the POS model for Indonesian and English that ships with [Flair](https://github.com/flairNLP/flair/). The architecture of this model uses **FastText**.
+
+- F-score (micro) = **0.9409**
+- F-score (macro) = **0.871**
+- Accuracy = **0.9409**
+
+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/).

model

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