--- language: - ar - ca - de - el - en - es - fr - hi - it - ja - ko - nl - pl - pt - ru - sv - vi - zh widget: - text: >- Els Red Hot Chili Peppers es van formar a Los Angeles per Kiedis, Flea, el guitarrista Hillel Slovak i el bateria Jack Irons. example_title: "Catalan" inference: parameters: decoder_start_token_id: 250058 src_lang: "ca_XX" tgt_lang: "" tags: - seq2seq - relation-extraction license: cc-by-nc-sa-4.0 pipeline_tag: translation --- # REDFM: a Filtered and Multilingual Relation Extraction Dataset This a multilingual version of [REBEL](https://huggingface.co/Babelscape/rebel-large). It can be used as a standalone multulingual Relation Extraction system, or as a pretrained system to be tuned on multilingual Relation Extraction datasets. mREBEL is introduced in the ACL 2023 paper [RED^{FM}: a Filtered and Multilingual Relation Extraction Dataset](https://github.com/Babelscape/rebel/blob/main/docs/). We present a new multilingual Relation Extraction dataset and train a multilingual version of REBEL which reframed Relation Extraction as a seq2seq task. The paper can be found [here](https://github.com/Babelscape/rebel/blob/main/docs/). If you use the code or model, please reference this work in your paper: @inproceedings{huguet-cabot-et-al-2023-red, title = "RED^{FM}: a Filtered and Multilingual Relation Extraction Dataset", author = "Huguet Cabot, Pere-Llu{\'\i}s and Navigli, Roberto", booktitle = "ACL 2023", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", } 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](https://huggingface.co/spaces/Babelscape/rebel-demo). ## Pipeline usage ```python from transformers import pipeline triplet_extractor = pipeline('translation_xx_to_yy', model='Babelscape/mrebel-large', tokenizer='Babelscape/mrebel-large') # We need to use the tokenizer manually since we need special tokens. extracted_text = triplet_extractor.tokenizer.batch_decode([triplet_extractor("The Red Hot Chili Peppers were formed in Los Angeles by Kiedis, Flea, guitarist Hillel Slovak and drummer Jack Irons.", decoder_start_token_id=250058, src_lang="en_XX", tgt_lang="", return_tensors=True, return_text=False)[0]["translation_token_ids"]]) # change en_XX for the language of the source. print(extracted_text[0]) # Function to parse the generated text and extract the triplets def extract_triplets_typed(text): triplets = [] relation = '' text = text.strip() current = 'x' subject, relation, object_, object_type, subject_type = '','','','','' for token in text.replace("", "").replace("", "").replace("", "").replace("tp_XX", "").replace("__en__", "").split(): if token == "" or token == "": current = 't' if relation != '': triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type}) relation = '' subject = '' elif token.startswith("<") and token.endswith(">"): if current == 't' or current == 'o': current = 's' if relation != '': triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type}) object_ = '' subject_type = token[1:-1] else: current = 'o' object_type = token[1:-1] relation = '' else: if current == 't': subject += ' ' + token elif current == 's': object_ += ' ' + token elif current == 'o': relation += ' ' + token if subject != '' and relation != '' and object_ != '' and object_type != '' and subject_type != '': triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type}) return triplets extracted_triplets = extract_triplets_typed(extracted_text[0]) print(extracted_triplets) ``` ## Model and Tokenizer using transformers ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer def extract_triplets_typed(text): triplets = [] relation = '' text = text.strip() current = 'x' subject, relation, object_, object_type, subject_type = '','','','','' for token in text.replace("", "").replace("", "").replace("", "").replace("tp_XX", "").replace("__en__", "").split(): if token == "" or token == "": current = 't' if relation != '': triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type}) relation = '' subject = '' elif token.startswith("<") and token.endswith(">"): if current == 't' or current == 'o': current = 's' if relation != '': triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type}) object_ = '' subject_type = token[1:-1] else: current = 'o' object_type = token[1:-1] relation = '' else: if current == 't': subject += ' ' + token elif current == 's': object_ += ' ' + token elif current == 'o': relation += ' ' + token if subject != '' and relation != '' and object_ != '' and object_type != '' and subject_type != '': triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type}) return triplets # Load model and tokenizer tokenizer = AutoTokenizer.from_pretrained("Babelscape/mrebel-large", src_lang="en_XX", "tgt_lang": "tp_XX") # Here we set English as source language. To change the source language just change it here or swap the first token of the input for your desired language model = AutoModelForSeq2SeqLM.from_pretrained("Babelscape/mrebel-large") gen_kwargs = { "max_length": 256, "length_penalty": 0, "num_beams": 3, "num_return_sequences": 3, "forced_bos_token_id": None, } # Text to extract triplets from text = 'The Red Hot Chili Peppers were formed in Los Angeles by Kiedis, Flea, guitarist Hillel Slovak and drummer Jack Irons.' # 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), decoder_start_token_id = self.tokenizer.convert_tokens_to_ids("tp_XX"), **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_typed(sentence)) ```