Update README.md

README.md

@@ -16,11 +16,16 @@ widget:
 # 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:
+
+## Original Datasets:
+- [AQMAR](http://www.cs.cmu.edu/~ark/ArabicNER/)
+- [ANERcorp](http://curtis.ml.cmu.edu/w/courses/index.php/ANERcorp)
+
+## Results:
 - F1-score (micro) 0.8666
 - F1-score (macro) 0.8488
 
-|      | tp  | fp | fn | precision | recall | class-F1 |
+|      | True Posititves  | False Positives | False Negatives | Precision | Recall | class-F1 |
 |------|-----|----|----|-----------|--------|----------|
 | LOC  | 539 | 51 | 68 | 0.9136    | 0.8880 | 0.9006   |
 | MISC | 408 | 57 | 89 | 0.8774    | 0.8209 | 0.8482   |
@@ -29,6 +34,71 @@ Results:
 
 ---
 
+# Usage
+```python
+from flair.data import Sentence
+from flair.models import SequenceTagger
+import pyarabic.araby as araby
+from flair.tokenization import JapaneseTokenizer
+from icecream import ic
+
+tagger = SequenceTagger.load("julien-c/flair-ner")
+arTagger = SequenceTagger.load('megantosh/flair-arabic-multi-ner')
+
+sentence = Sentence('George Washington went to Washington .')
+arSentence = Sentence('عمرو عادلي أستاذ للاقتصاد السياسي المساعد في الجامعة الأمريكية  بالقاهرة .')
+
+
+# predict NER tags
+tagger.predict(sentence)
+arTagger.predict(arSentence)
+
+# print sentence with predicted tags
+ic(sentence.to_tagged_string)
+ic(arSentence.to_tagged_string)
+
+```
+
+# Example
+```bash
+2021-07-07 14:30:59,649 loading file /Users/mega/.flair/models/flair-ner/f22eb997f66ae2eacad974121069abaefca5fe85fce71b49e527420ff45b9283.941c7c30b38aef8d8a4eb5c1b6dd7fe8583ff723fef457382589ad6a4e859cfc
+2021-07-07 14:31:04,654 loading file /Users/mega/.flair/models/flair-arabic-multi-ner/c7af7ddef4fdcc681fcbe1f37719348afd2862b12aa1cfd4f3b93bd2d77282c7.242d030cb106124f7f9f6a88fb9af8e390f581d42eeca013367a86d585ee6dd6
+ic| sentence.to_tagged_string: <bound method Sentence.to_tagged_string of Sentence: "George Washington went to Washington ."   [− Tokens: 6  − Token-Labels: "George <B-PER> Washington <E-PER> went to Washington <S-LOC> ."]>
+ic| arSentence.to_tagged_string: <bound method Sentence.to_tagged_string of Sentence: "عمرو عادلي أستاذ للاقتصاد السياسي المساعد في الجامعة الأمريكية بالقاهرة ."   [− Tokens: 11  − Token-Labels: "عمرو <B-PER> عادلي <I-PER> أستاذ للاقتصاد السياسي المساعد في الجامعة <B-ORG> الأمريكية <I-ORG> بالقاهرة <B-LOC> ."]>
+ic| entity: <PER-span (1,2): "George Washington">
+ic| entity: <LOC-span (5): "Washington">
+ic| entity: <PER-span (1,2): "عمرو عادلي">
+ic| entity: <ORG-span (8,9): "الجامعة الأمريكية">
+ic| entity: <LOC-span (10): "بالقاهرة">
+ic| sentence.to_dict(tag_type='ner'): 
+{"text":"عمرو عادلي أستاذ للاقتصاد السياسي المساعد في الجامعة الأمريكية  بالقاهرة .",
+"labels":[],
+{"entities":[{{{
+               "text":"عمرو عادلي",
+               "start_pos":0,
+               "end_pos":10,
+               "labels":[PER (0.9826)]},
+            {"text":"الجامعة الأمريكية",
+               "start_pos":45,
+               "end_pos":62,
+               "labels":[ORG (0.7679)]},
+            {"text":"بالقاهرة",
+               "start_pos":64,
+               "end_pos":72,
+               "labels":[LOC (0.8079)]}]}
+"text":"George Washington went to Washington .",
+"labels":[],
+"entities":[{
+           {"text":"George Washington",
+            "start_pos":0,
+            "end_pos":17,
+            "labels":[PER (0.9968)]},
+           {"text":"Washington""start_pos":26,
+            "end_pos":36,
+            "labels":[LOC (0.9994)]}}]}
+```
+
+# Configuration
 ```
 2020-10-27 12:05:47,801 Model: "SequenceTagger(
   (embeddings): StackedEmbeddings(
@@ -59,4 +129,15 @@ Results:
   (weights): None
   (weight_tensor) None
   
- ```
+ ```
+ 
+ 
+ # Citation
+*if you use this model in your work, please consider citing this work:*
+```latex
+@unpublished{MMHU21
+author = "M. Megahed and A. Akbik",
+title = "Sequence Labeling Architectures in Diglossia",
+note = "In preparation",
+}
+```