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