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--- |
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language: |
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- en |
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widget: |
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- text: "Punta Cana is a resort town in the municipality of Higuey, in La Altagracia Province, the eastern most province of the Dominican Republic" |
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tags: |
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- seq2seq |
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- relation-extraction |
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datasets: |
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- Babelscape/rebel-dataset |
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model-index: |
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- name: REBEL |
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results: |
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- task: |
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name: Relation Extraction |
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type: Relation-Extraction |
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dataset: |
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name: "CoNLL04" |
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type: CoNLL04 |
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metrics: |
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- name: RE+ Macro F1 |
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type: re+ macro f1 |
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value: 76.65 |
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- task: |
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name: Relation Extraction |
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type: Relation-Extraction |
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dataset: |
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name: "NYT" |
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type: NYT |
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metrics: |
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- name: F1 |
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type: f1 |
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value: 93.4 |
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license: cc-by-nc-sa-4.0 |
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--- |
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[](https://paperswithcode.com/sota/relation-extraction-on-nyt?p=rebel-relation-extraction-by-end-to-end) |
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[](https://paperswithcode.com/sota/relation-extraction-on-conll04?p=rebel-relation-extraction-by-end-to-end) |
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[](https://paperswithcode.com/sota/joint-entity-and-relation-extraction-on-3?p=rebel-relation-extraction-by-end-to-end) |
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[](https://paperswithcode.com/sota/relation-extraction-on-ade-corpus?p=rebel-relation-extraction-by-end-to-end) |
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[](https://paperswithcode.com/sota/relation-extraction-on-re-tacred?p=rebel-relation-extraction-by-end-to-end) |
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# REBEL: Relation Extraction By End-to-end Language generation |
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This is the model card for the Findings of EMNLP 2021 paper [REBEL: Relation Extraction By End-to-end Language generation](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf). We present a new linearization aproach and a reframing of Relation Extraction as a seq2seq task. The paper can be found [here](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf). If you use the code, please reference this work in your paper: |
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@inproceedings{huguet-cabot-navigli-2021-rebel, |
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title = "REBEL: Relation Extraction By End-to-end Language generation", |
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author = "Huguet Cabot, Pere-Llu{\'\i}s and |
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Navigli, Roberto", |
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booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", |
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month = nov, |
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year = "2021", |
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address = "Online and in the Barceló Bávaro Convention Centre, Punta Cana, Dominican Republic", |
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publisher = "Association for Computational Linguistics", |
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url = "https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf", |
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} |
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The original repository for the paper can be found [here](https://github.com/Babelscape/rebel) |
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Be aware that the inference widget at the right does not output special tokens, which are necessary to distinguish the subject, object and relation types. For a demo of REBEL and its pre-training dataset check the [Spaces demo](Babelscape/rebel-demo). |
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## Pipeline usage |
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```python |
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from transformers import pipeline |
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triplet_extractor = pipeline('text2text-generation', model='Babelscape/rebel-large', tokenizer='Babelscape/rebel-large') |
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# We need to use the tokenizer manually since we need special tokens. |
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extracted_text = triplet_extractor.tokenizer.batch_decode(triplet_extractor("Punta Cana is a resort town in the municipality of Higuey, in La Altagracia Province, the eastern most province of the Dominican Republic", return_tensors=True, return_text=False)[0]["generated_token_ids"]["output_ids"]) |
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print(extracted_text[0]) |
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# Function to parse the generated text and extract the triplets |
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def extract_triplets(text): |
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triplets = [] |
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relation, subject, relation, object_ = '', '', '', '' |
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text = text.strip() |
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current = 'x' |
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for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").split(): |
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if token == "<triplet>": |
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current = 't' |
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if relation != '': |
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triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) |
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relation = '' |
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subject = '' |
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elif token == "<subj>": |
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current = 's' |
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if relation != '': |
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triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) |
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object_ = '' |
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elif token == "<obj>": |
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current = 'o' |
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relation = '' |
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else: |
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if current == 't': |
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subject += ' ' + token |
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elif current == 's': |
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object_ += ' ' + token |
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elif current == 'o': |
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relation += ' ' + token |
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if subject != '' and relation != '' and object_ != '': |
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triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) |
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return triplets |
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extracted_triplets = extract_triplets(extracted_text[0]) |
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print(extracted_triplets) |
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``` |
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## Model and Tokenizer using transformers |
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```python |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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def extract_triplets(text): |
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triplets = [] |
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relation, subject, relation, object_ = '', '', '', '' |
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text = text.strip() |
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current = 'x' |
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for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").split(): |
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if token == "<triplet>": |
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current = 't' |
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if relation != '': |
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triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) |
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relation = '' |
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subject = '' |
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elif token == "<subj>": |
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current = 's' |
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if relation != '': |
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triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) |
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object_ = '' |
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elif token == "<obj>": |
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current = 'o' |
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relation = '' |
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else: |
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if current == 't': |
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subject += ' ' + token |
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elif current == 's': |
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object_ += ' ' + token |
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elif current == 'o': |
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relation += ' ' + token |
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if subject != '' and relation != '' and object_ != '': |
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triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) |
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return triplets |
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# Load model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("Babelscape/rebel-large") |
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model = AutoModelForSeq2SeqLM.from_pretrained("Babelscape/rebel-large") |
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gen_kwargs = { |
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"max_length": 256, |
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"length_penalty": 0, |
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"num_beams": 3, |
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"num_return_sequences": 3, |
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} |
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# Text to extract triplets from |
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text = 'Punta Cana is a resort town in the municipality of Higüey, in La Altagracia Province, the easternmost province of the Dominican Republic.' |
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# Tokenizer text |
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model_inputs = tokenizer(text, max_length=256, padding=True, truncation=True, return_tensors = 'pt') |
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# Generate |
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generated_tokens = model.generate( |
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model_inputs["input_ids"].to(model.device), |
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attention_mask=model_inputs["attention_mask"].to(model.device), |
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**gen_kwargs, |
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) |
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# Extract text |
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decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=False) |
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# Extract triplets |
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for idx, sentence in enumerate(decoded_preds): |
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print(f'Prediction triplets sentence {idx}') |
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print(extract_triplets(sentence)) |
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``` |
