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---
tags:
- BERT
- Text Classification
- relation
language: Arabic
license: mit
datasets:
- ACE2005
---
# Arabic Relation Extraction Model
- [Github repo](https://github.com/edchengg/GigaBERT)
- 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")
>>> ner_tokenizer = AutoTokenizer.from_pretrained("ychenNLP/arabic-ner-ace")
>>> ner_pip = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, grouped_entities=True)
>>> re_model = AutoModelForSequenceClassification.from_pretrained("ychenNLP/arabic-relation-extraction")
>>> re_tokenizer = AutoTokenizer.from_pretrained("ychenNLP/arabic-relation-extraction")
>>> re_pip = pipeline("text-classification", model=re_model, tokenizer=re_tokenizer)
def process_ner_output(entity_mention, inputs):
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(inputs):
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": inputs})
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}
}
```
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