| | --- |
| | language: |
| | - ar |
| | - ca |
| | - de |
| | - el |
| | - en |
| | - es |
| | - fr |
| | - hi |
| | - it |
| | - ja |
| | - ko |
| | - nl |
| | - pl |
| | - pt |
| | - ru |
| | - sv |
| | - vi |
| | - zh |
| | widget: |
| | - text: >- |
| | The Red Hot Chili Peppers were formed in Los Angeles by Kiedis, Flea, guitarist Hillel Slovak and drummer Jack Irons. |
| | example_title: English |
| | inference: |
| | parameters: |
| | src_lang: en |
| | tgt_lang: en |
| | tags: |
| | - seq2seq |
| | - relation-extraction |
| | license: cc-by-nc-sa-4.0 |
| | pipeline_tag: translation |
| | --- |
| | # RED<sup>FM</sup>: a Filtered and Multilingual Relation Extraction Dataset |
| |
|
| | This is 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://arxiv.org/abs/2306.09802). 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://arxiv.org/abs/2306.09802). If you use the code or model, please reference this work in your paper: |
| |
|
| | @inproceedings{huguet-cabot-et-al-2023-redfm-dataset, |
| | title = "RED$^{\rm FM}$: a Filtered and Multilingual Relation Extraction Dataset", |
| | author = "Huguet Cabot, Pere-Llu{\'\i}s and Tedeschi, Simone and Ngonga Ngomo, Axel-Cyrille and |
| | Navigli, Roberto", |
| | booktitle = "Proc. of the 61st Annual Meeting of the Association for Computational Linguistics: ACL 2023", |
| | month = jul, |
| | year = "2023", |
| | address = "Toronto, Canada", |
| | publisher = "Association for Computational Linguistics", |
| | url = "https://arxiv.org/abs/2306.09802", |
| | } |
| | |
| | The original repository for the paper can be found [here](https://github.com/Babelscape/rebel#REDFM) |
| |
|
| | 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 mREBEL and its pre-training dataset check the [Spaces demo](https://huggingface.co/spaces/Babelscape/mrebel-demo). |
| |
|
| | ## Pipeline usage |
| |
|
| | ```python |
| | from transformers import pipeline |
| | |
| | triplet_extractor = pipeline('translation_xx_to_yy', model='Babelscape/mrebel-base', tokenizer='Babelscape/mrebel-base') |
| | # 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.", src_lang="en", return_tensors=True, return_text=False)[0]["translation_token_ids"]]) # change __en__ 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("<s>", "").replace("<pad>", "").replace("</s>", "").replace("tp_XX", "").replace("__en__", "").split(): |
| | if token == "<triplet>" or token == "<relation>": |
| | 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("<s>", "").replace("<pad>", "").replace("</s>", "").replace("tp_XX", "").replace("__en__", "").split(): |
| | if token == "<triplet>" or token == "<relation>": |
| | 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-base", src_lang="en", tgt_lang="en") |
| | # Here we set English ("en") as source language. To change the source language swap the first token of the input for your desired language or change to supported language. |
| | model = AutoModelForSeq2SeqLM.from_pretrained("Babelscape/mrebel-base") |
| | 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), |
| | **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)) |
| | ``` |
| |
|
| | ## License |
| |
|
| | This model is licensed under the CC BY-SA 4.0 license. The text of the license can be found [here](https://creativecommons.org/licenses/by-nc-sa/4.0/). |
