Model Overview
Model Features
Model Capabilities
Use Cases
🚀 REDFM: a Filtered and Multilingual Relation Extraction Dataset
REDFM is a multilingual relation extraction dataset. It can be used as a standalone system or for pretraining on multilingual relation extraction tasks.
Supported Languages
Property | Details |
---|---|
Supported Languages | ar, ca, de, el, en, es, fr, hi, it, ja, ko, nl, pl, pt, ru, sv, vi, zh |
Widget Example
The widget provides an example in Catalan:
- Example Title: Catalan
- Text: Els Red Hot Chili Peppers es van formar a Los Angeles per Kiedis, Flea, el guitarrista Hillel Slovak i el bateria Jack Irons.
Inference Parameters
Property | Details |
---|---|
Decoder Start Token ID | 250058 |
Source Language | ca_XX |
Target Language |
Tags
- seq2seq
- relation-extraction
License
This model is licensed under the CC BY-NC-SA 4.0 license.
Pipeline Tag
translation
Datasets
- Babelscape/SREDFM
🚀 Quick Start
This is a multilingual version of REBEL. It can serve as a standalone multilingual Relation Extraction system or as a pretrained system for fine - tuning on multilingual Relation Extraction datasets.
mREBEL is introduced in the ACL 2023 paper RED^{FM}: a Filtered and Multilingual Relation Extraction Dataset. We present a new multilingual Relation Extraction dataset and train a multilingual version of REBEL, which reframes Relation Extraction as a seq2seq task. The paper can be found here. 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.
⚠️ Important Note
The inference widget on 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.
💻 Usage Examples
Basic Usage
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="<triplet>", 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("<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)
Advanced Usage
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-large", src_lang="en_XX", tgt_lang="tp_XX")
# Here we set English ("en_XX") as source language. To change the source language swap the first token of the input for your desired language or change to supported language. For catalan ("ca_XX") or greek ("el_EL") (not included in mBART pretraining) you need a workaround:
# tokenizer._src_lang = "ca_XX"
# tokenizer.cur_lang_code_id = tokenizer.convert_tokens_to_ids("ca_XX")
# tokenizer.set_src_lang_special_tokens("ca_XX")
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 = 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))
📄 License
This model is licensed under the CC BY - SA 4.0 license.





