README.md

metadata

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

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. We present a new linearization aproach and a reframing of Relation Extraction as a seq2seq task. The paper can be found here. 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

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.

Pipeline usage

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("<s>", "").replace("<pad>", "").replace("</s>", "").split():
        if token == "<triplet>":
            current = 't'
            if relation != '':
                triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
                relation = ''
            subject = ''
        elif token == "<subj>":
            current = 's'
            if relation != '':
                triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
            object_ = ''
        elif token == "<obj>":
            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

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

def extract_triplets(text):
    triplets = []
    relation, subject, relation, object_ = '', '', '', ''
    text = text.strip()
    current = 'x'
    for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").split():
        if token == "<triplet>":
            current = 't'
            if relation != '':
                triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
                relation = ''
            subject = ''
        elif token == "<subj>":
            current = 's'
            if relation != '':
                triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
            object_ = ''
        elif token == "<obj>":
            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))