Update README.md

README.md

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 ---
+tags:
+- BERT
+- Text Classification
+language: Arabic
 license: mit
+datasets:
+- ACE2005
 ---
+
+# Arabic Relation Extraction Model
+- Relation Extraction model based on [GigaBERTv4](https://huggingface.co/lanwuwei/GigaBERT-v4-Arabic-and-English).
+- ACE2005 Training data: Arabic
+- [Relation tags](https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/arabic-relations-guidelines-v6.5.pdf) including: Physical, Part-whole, Personal-Social, 'ORG-Affiliation, Agent-Artifact, Gen-Affiliation
+
+## Hyperparameters
+- learning_rate=2e-5
+- num_train_epochs=10
+- weight_decay=0.01
+
+## ACE2005 Evaluation results (F1)
+| Language | Arabic | 
+|:----:|:-----------:|
+|      | 89.4   |
+
+## How to use
+```python
+>>> from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer, AuotoModelForSequenceClassification
+
+>>> ner_model = AutoModelForTokenClassification.from_pretrained("ychenNLP/arabic-ner-ace-gigabert")
+>>> ner_tokenizer = AutoTokenizer.from_pretrained("ychenNLP/arabic-ner-ace-gigabert")
+>>> ner_pip = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, grouped_entities=True)
+
+>>> re_model = AutoModelForSequenceClassification.from_pretrained("ychenNLP/arabic-relation-extraction-ace-gigabert")
+>>> re_tokenizer = AutoTokenizer.from_pretrained("ychenNLP/arabic-relation-extraction-ace-gigabert")
+>>> re_pip = pipeline("text-classification", model=re_model, tokenizer=re_tokenizer)
+
+def process_ner_output(entity_mention, input):
+    re_input = []
+    for idx1 in range(len(entity_mention) - 1):
+        for idx2 in range(idx1 + 1, len(entity_mention)):
+            ent_1 = entity_mention[idx1]
+            ent_2 = entity_mention[idx2]
+
+            ent_1_type = ent_1['entity_group']
+            ent_2_type = ent_2['entity_group']
+            ent_1_s = ent_1['start']
+            ent_1_e = ent_1['end']
+            ent_2_s = ent_2['start']
+            ent_2_e = ent_2['end']
+            new_re_input = ""
+            for c_idx, c in enumerate(input):
+                if c_idx == ent_1_s:
+                    new_re_input += "<{}>".format(ent_1_type)
+                elif c_idx == ent_1_e:
+                    new_re_input += "</{}>".format(ent_1_type)
+                elif c_idx == ent_2_s:
+                    new_re_input += "<{}>".format(ent_2_type)
+                elif c_idx == ent_2_e:
+                    new_re_input += "</{}>".format(ent_2_type)
+                new_re_input += c
+            re_input.append({"re_input": new_re_input, "arg1": ent_1, "arg2": ent_2, "input": input})
+    return re_input
+    
+def post_process_re_output(re_output, re_input, ner_output):
+    final_output = []
+    for idx, out in enumerate(re_output):
+        if out["label"] != 'O':
+            tmp = re_input[idx]
+            tmp['relation_type'] = out
+            tmp.pop('re_input', None)
+            final_output.append(tmp)
+
+    template = {"input": re_input["input"],
+                "entity": ner_output,
+                "relation": final_output}
+
+    return template
+
+>>> input = "Hugging face is a French company in New york."
+>>> output = ner_pip(input)
+
+>>> re_input = process_ner_output(output, input)
+
+>>> re_output = []
+>>> for idx in range(len(re_input)):
+>>>     tmp_re_output = re_pip(re_input[idx]["re_input"])
+>>>     re_output.append(tmp_re_output)
+
+
+
+>>> re_ner_output = post_process_re_output(re_output)
+>>> print("Sentence: ",re_ner_output["input"])
+>>> print("Entity: ", re_ner_output["entity"])
+>>> print("Relation: ", re_ner_output["relation"])
+
+### BibTeX entry and citation info
+
+```bibtex
+@inproceedings{lan2020gigabert,
+  author     = {Lan, Wuwei and Chen, Yang and Xu, Wei and Ritter, Alan},
+    title      = {Giga{BERT}: Zero-shot Transfer Learning from {E}nglish to {A}rabic},
+    booktitle  = {Proceedings of The 2020 Conference on Empirical Methods on Natural Language Processing (EMNLP)},
+    year       = {2020}
+  } 
+```