Mrebel Large 32

🚀 RED^FM：フィルタリングされた多言語関係抽出データセット

これはREBELの多言語バージョンです。独立した多言語関係抽出システムとして使用することも、多言語関係抽出データセットで微調整するための事前学習済みシステムとして使用することもできます。

mREBELは、ACL 2023の論文RED^{FM}: a Filtered and Multilingual Relation Extraction Datasetで紹介されました。新しい多言語関係抽出データセットを提示し、関係抽出をseq2seqタスクとして再構築したREBELの多言語バージョンを学習させました。論文はこちらで見ることができます。コードやモデルを使用する場合は、論文でこの研究を引用してください。

@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",
}

論文のオリジナルリポジトリはこちらで見ることができます。

右側の推論ウィジェットは、主語、目的語、関係タイプを区別するために必要な特殊トークンを出力しません。mREBELとその事前学習データセットのデモは、Spacesデモを確認してください。

🚀 クイックスタート

💻 使用例

基本的な使用法

from transformers import pipeline

triplet_extractor = pipeline('translation_xx_to_yy', model='Babelscape/mrebel-large-32', tokenizer='Babelscape/mrebel-large-32')
# 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)

高度な使用法

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-32", 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-32")
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))

📄 ライセンス

このモデルはCC BY - SA 4.0ライセンスの下で提供されています。ライセンスのテキストはこちらで確認できます。