---
language: ar
license: apache-2.0
datasets:
- AQMAR
- ANERcorp
embeddings:
- GloVe
- Flair
---
# Arabic NER Model using Flair Embeddings
Training was conducted over 94 epochs, using a linear decaying learning rate of 2e-05, starting from 0.225 and a batch size of 32 with GloVe and Flair forward and backward embeddings.

Results:
- F1-score (micro) 0.8666
- F1-score (macro) 0.8488

|      | tp  | fp | fn | precision | recall | class-F1 |
|------|-----|----|----|-----------|--------|----------|
| LOC  | 539 | 51 | 68 | 0.9136    | 0.8880 | 0.9006   |
| MISC | 408 | 57 | 89 | 0.8774    | 0.8209 | 0.8482   |
| ORG  | 167 | 43 | 64 | 0.7952    | 0.7229 | 0.7574   |
| PER  | 501 | 65 | 60 | 0.8852    | 0.8930 | 0.8891   |

---

```
2020-10-27 12:05:47,801 Model: "SequenceTagger(
  (embeddings): StackedEmbeddings(
    (list_embedding_0): WordEmbeddings('glove')
    (list_embedding_1): FlairEmbeddings(
      (lm): LanguageModel(
        (drop): Dropout(p=0.1, inplace=False)
        (encoder): Embedding(7125, 100)
        (rnn): LSTM(100, 2048)
        (decoder): Linear(in_features=2048, out_features=7125, bias=True)
      )
    )
    (list_embedding_2): FlairEmbeddings(
      (lm): LanguageModel(
        (drop): Dropout(p=0.1, inplace=False)
        (encoder): Embedding(7125, 100)
        (rnn): LSTM(100, 2048)
        (decoder): Linear(in_features=2048, out_features=7125, bias=True)
      )
    )
  )
  (word_dropout): WordDropout(p=0.05)
  (locked_dropout): LockedDropout(p=0.5)
  (embedding2nn): Linear(in_features=4196, out_features=4196, bias=True)
  (rnn): LSTM(4196, 256, batch_first=True, bidirectional=True)
  (linear): Linear(in_features=512, out_features=15, bias=True)
  (beta): 1.0
  (weights): None
  (weight_tensor) None
  
 ```