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--- |
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language: |
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- ar |
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- ca |
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- de |
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- el |
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- en |
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- es |
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- fr |
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- hi |
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- it |
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- ja |
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- ko |
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- nl |
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- pl |
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- pt |
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- ru |
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- sv |
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- vi |
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- zh |
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widget: |
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- text: >- |
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Els Red Hot Chili Peppers es van formar a Los Angeles per Kiedis, Flea, el guitarrista Hillel Slovak i el bateria Jack Irons. |
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example_title: "Catalan" |
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inference: |
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parameters: |
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decoder_start_token_id: 250058 |
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src_lang: "ca_XX" |
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tgt_lang: "<triplet>" |
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tags: |
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- seq2seq |
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- relation-extraction |
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license: cc-by-nc-sa-4.0 |
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pipeline_tag: translation |
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--- |
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# RED<sup>FM</sup>: a Filtered and Multilingual Relation Extraction Dataset |
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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. |
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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: |
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@inproceedings{huguet-cabot-et-al-2023-redfm-dataset, |
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title = "RED$^{\rm FM}$: a Filtered and Multilingual Relation Extraction Dataset", |
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author = "Huguet Cabot, Pere-Llu{\'\i}s and Tedeschi, Simone and Ngonga Ngomo, Axel-Cyrille and |
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Navigli, Roberto", |
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booktitle = "Proc. of the 61st Annual Meeting of the Association for Computational Linguistics: ACL 2023", |
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month = jul, |
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year = "2023", |
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address = "Toronto, Canada", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/2306.09802", |
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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](https://huggingface.co/spaces/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('translation_xx_to_yy', model='Babelscape/mrebel-large', tokenizer='Babelscape/mrebel-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("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="<triplet>", return_tensors=True, return_text=False)[0]["translation_token_ids"]]) # change en_XX for the language of the source. |
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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_typed(text): |
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triplets = [] |
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relation = '' |
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text = text.strip() |
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current = 'x' |
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subject, relation, object_, object_type, subject_type = '','','','','' |
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for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").replace("tp_XX", "").replace("__en__", "").split(): |
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if token == "<triplet>" or token == "<relation>": |
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current = 't' |
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if relation != '': |
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triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type}) |
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relation = '' |
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subject = '' |
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elif token.startswith("<") and token.endswith(">"): |
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if current == 't' or current == 'o': |
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current = 's' |
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if relation != '': |
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triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type}) |
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object_ = '' |
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subject_type = token[1:-1] |
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else: |
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current = 'o' |
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object_type = token[1:-1] |
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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_ != '' and object_type != '' and subject_type != '': |
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triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type}) |
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return triplets |
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extracted_triplets = extract_triplets_typed(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_typed(text): |
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triplets = [] |
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relation = '' |
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text = text.strip() |
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current = 'x' |
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subject, relation, object_, object_type, subject_type = '','','','','' |
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for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").replace("tp_XX", "").replace("__en__", "").split(): |
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if token == "<triplet>" or token == "<relation>": |
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current = 't' |
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if relation != '': |
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triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type}) |
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relation = '' |
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subject = '' |
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elif token.startswith("<") and token.endswith(">"): |
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if current == 't' or current == 'o': |
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current = 's' |
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if relation != '': |
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triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type}) |
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object_ = '' |
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subject_type = token[1:-1] |
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else: |
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current = 'o' |
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object_type = token[1:-1] |
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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_ != '' and object_type != '' and subject_type != '': |
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triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type}) |
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return triplets |
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# Load model and tokenizer |
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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 |
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model = AutoModelForSeq2SeqLM.from_pretrained("Babelscape/mrebel-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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"forced_bos_token_id": None, |
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} |
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# Text to extract triplets from |
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text = 'The Red Hot Chili Peppers were formed in Los Angeles by Kiedis, Flea, guitarist Hillel Slovak and drummer Jack Irons.' |
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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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decoder_start_token_id = self.tokenizer.convert_tokens_to_ids("tp_XX"), |
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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_typed(sentence)) |
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``` |
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## License |
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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/). |
